Use of Artificial Intelligence in Engineering Practice
The Board cited this case to establish historical precedent for the ethical use of computer-assisted drafting and design tools, and to show that the BER has long grappled with questions about new technology in engineering practice, including early references to AI.
The Board cited this case to draw a parallel to Engineer A's use of AI-assisted drafting tools without sufficient competency verification, and to reinforce the principle that technology must not replace or substitute for engineering judgment, while also distinguishing Engineer A as not wholly incompetent unlike the engineer in that case.
CausalLink_AI Disclosure Omission for Des
CausalLink_AI Disclosure Omission for Rep
Because the AI tool adoption directly caused the report generation and its stylistic inconsistencies, concealing the AI's role deceives the client about the true authorship and quality basis of the deliverable, which undermines the trust that professional attribution obligations are designed to protect.
Using AI for design generation under dual deliverable pressure carries normative weight because the causal chain produces AI-generated design outputs that feed into a review process already shown to be cursory, meaning the absence of a clear fulfillment or violation here reflects a gap in oversight that amplifies the risk of undetected design defects reaching the client.
Adopting the AI tool for the report fulfils competence obligations by leveraging available technology, but the causal chain shows it directly produces an AI Disclosure Omission, meaning the fulfillment of competence is undermined by a transparency failure that leaves the client and public unaware of how the deliverable was generated.
Uploading client data to the AI tool violates confidentiality because it causally produces client data exposure to a third-party system, and this breach is not offset by the downstream report generation benefit, since the client never consented to their proprietary information leaving the engineer's control.
CausalLink_Cursory Design Document Review
The cursory review caused a design defect to be discovered only after submission, which triggered a client revision instruction, meaning the failure to exercise adequate direction and control over the work product directly produced a safety-relevant gap that the responsible charge obligation exists precisely to prevent.
Submitting the design document without adequate review or qualification fulfils no protective obligation and instead places a potentially defective design into the project record, meaning the public safety and responsible charge violations are not merely procedural but carry real downstream consequences given that a defect was in fact present.
CausalLink_Draft Report Sealing and Submi
Sealing and submitting the report gives it the force of a professional certification, so violating credit and attribution obligations at this step matters because it formally commits Engineer A to standing behind a product whose actual origins were misrepresented, compounding the downstream stylistic inconsistency into a documented professional record.
Accepting the project within the bounds of competence matters because it establishes the foundational professional commitment to public safety, and any downstream failures in design review or AI tool use trace back to whether the engineer had the genuine capability to oversee the full scope of work from the outset.
Conducting a thorough review fulfils both direction and control over the work product and competence obligations, and this matters causally because the report stylistic inconsistency produced by AI generation and draft sealing could have been caught and corrected before submission, making the review the critical intervention point for maintaining professional accountability.
Engineer A's use of AI in report writing was partly ethical, and partly unethical.
Beyond the Board's finding that Engineer A's use of AI in report writing was partly ethical and partly unethical, the most independently significant ethical breach was not the use of AI itself but rather Engineer A's upload of Client W's confidential site data and groundwater monitoring information into an open-source AI platform without obtaining Client W's prior consent. This act violated Code Section II.1.c regardless of the quality of the resulting report or the thoroughness of Engineer A's subsequent review. The confidentiality breach is analytically separable from the competence and disclosure questions: even if Engineer A had produced a flawless, fully attributed report, the unauthorized exposure of proprietary client data to an open-source system with unknown data retention and third-party access policies would remain an independent ethical violation. The Board's conclusion that the report submission was partly ethical should therefore be understood as resting on a foundation that was itself compromised before Engineer A wrote a single word of review commentary.
The Board's finding that AI-assisted drafting tools are not unethical per se must be qualified by a competence threshold that Engineer A failed to meet before deploying those tools on a live client engagement. Code Sections I.2 and II.2.a require engineers to undertake assignments only when qualified by education or experience in the specific technical field involved. This obligation extends to the tools an engineer selects to perform that work: adopting AI drafting software that was entirely new to the market, with which Engineer A had no prior experience, for the purpose of generating sealed engineering design documents, is not ethically equivalent to using a mature, well-understood software application. The analogy the Board drew between AI tools and conventional engineering software—such as CAD or finite element analysis programs—understates the difference in verification burden. Established engineering software produces deterministic outputs that engineers can validate against known benchmarks; large-language-model and AI-drafting outputs are probabilistic, context-sensitive, and capable of producing plausible-looking but technically incorrect results, as the design deficiencies in this case demonstrated. Engineer A's lack of tool-specific proficiency therefore created a foreseeable competence gap that the cursory design review did not close, and the Board's permissive conclusion about AI tool use should not be read to excuse adoption of unfamiliar AI tools without a minimum threshold of tool-specific qualification.
The Board's conclusion that Engineer A had no professional or ethical obligation to disclose AI use in the report—by analogy to conventional engineering software—does not adequately account for the distinct authorship and attribution dimensions raised by large-language-model text generation. Code Section III.9 requires engineers to give credit for engineering work to those to whom credit is due. When an AI system generates the substantive prose of a professional report, the question of authorship credit is not merely a stylistic preference but a professional representation about the origin of the intellectual work product. Client W's observation that the report read as if written by two different authors is not a trivial aesthetic complaint: it is evidence that the AI-generated sections were perceptibly distinct in character from Engineer A's own writing, which means the report implicitly represented a unified human authorship that did not exist. This implicit misrepresentation, even absent an affirmative false statement, is in tension with Code Sections I.5 and III.3, which prohibit deceptive acts and conduct that deceives the public. The Board's non-disclosure conclusion should therefore be treated as contingent on circumstances where AI involvement is not perceptible and does not create a false impression of authorship—conditions that were not met in this case.
The Board's partial ethical approval of Engineer A's report work—grounded in Engineer A's thorough cross-checking—should not be extended to the engineering design documents, where the standard of review was categorically different and the consequences of error were categorically more serious. For the report, Engineer A verified factual claims against journal articles and checked phrasing for originality; this is a form of substantive intellectual engagement with the AI output. For the design documents, Engineer A conducted only a cursory review and adjusted certain elements for site-specific conditions, yet affixed a professional seal. Code Section II.2.b prohibits engineers from signing plans dealing with subject matter in which they lack competence, and the act of sealing is a public representation that the engineer has exercised responsible charge over the work. A cursory review of AI-generated plans that contained misaligned dimensions and omitted safety features required by local regulations does not constitute responsible charge; it constitutes a delegation of engineering judgment to an unqualified automated system. The seal therefore misrepresented the degree of professional oversight actually applied, creating a risk to public safety that the Board correctly identified as an ethical violation but that deserves emphasis as the most serious failure in this case.
A dimension the Board did not address is the structural role that Engineer B's retirement played in creating the conditions for Engineer A's ethical failures. Engineer A's loss of mentorship and quality assurance review was not merely a personal inconvenience but a foreseeable competence gap that Engineer A was obligated to address through affirmative professional measures before accepting or continuing sealed deliverable work. The NSPE Code's competence obligations are not satisfied by the historical fact of prior mentored experience; they require that an engineer be currently qualified to perform the work independently or with appropriate oversight at the time of performance. Engineer A's response to the mentorship gap—unilaterally adopting an unfamiliar open-source AI tool as a functional substitute for peer review—was not a professionally adequate adaptation. Ethically sound alternatives would have included engaging a qualified substitute reviewer, limiting the scope of accepted work to tasks within Engineer A's independent competence, or declining the engagement pending identification of appropriate oversight. The Board's analysis would benefit from explicitly recognizing that the mentor loss was not a mitigating circumstance but a triggering condition that elevated Engineer A's professional obligations rather than relaxing them.
The Board's discretionary approach to AI disclosure—treating it as ethically favored but not categorically required absent contractual obligation—produces an unstable standard that is likely to erode client trust and professional accountability as AI tools become more capable and more widely used. From a consequentialist perspective, a universal norm requiring disclosure whenever AI plays a substantial role in generating a sealed work product would produce better outcomes across three dimensions: it would enable clients to make informed decisions about the work product they are receiving; it would create professional incentives for engineers to maintain genuine responsible charge rather than delegating judgment to AI systems; and it would allow the profession to develop empirical knowledge about where AI-assisted engineering succeeds and fails. The Board's analogy to conventional engineering software is likely to become increasingly untenable as AI systems move from computation tools to generative authorship tools, and the profession would benefit from proactively establishing disclosure norms before the absence of such norms produces more serious public safety failures than the design deficiencies observed in this case.
The use of AI-assisted drafting tools by Engineer A was not unethical per se.
In response to Q101: Engineer A's upload of Client W's confidential site data and groundwater monitoring information into an open-source AI platform without obtaining Client W's prior consent constitutes an independent and serious breach of the client confidentiality obligation under Code Section II.1.c. Open-source AI platforms, by their nature, may retain, process, or expose input data in ways that fall outside the engineer's control, and Engineer A's unfamiliarity with the tool's full functionality compounds this risk. This breach is analytically separable from the quality of Engineer A's subsequent review of the report: even if the report were technically flawless, the act of exposing confidential client data to an uncontrolled third-party platform without consent is independently unethical. The breach does not become retroactively permissible because no confirmed data leak occurred; the obligation under II.1.c is triggered by unauthorized disclosure, not by demonstrated harm. Accordingly, the submission of the report is rendered ethically deficient on confidentiality grounds alone, independent of any assessment of report quality or AI disclosure obligations.
In response to Q102: Engineer A did not satisfy the competence requirement under Code Sections I.2 and II.2.a before adopting the AI-assisted drafting tool for a live client engagement. The tool was new to the market, Engineer A had no prior experience with it, and Engineer A's own unfamiliarity with its full functionality is explicitly acknowledged in the case facts. Competence under the Code is not limited to domain expertise in environmental engineering; it extends to the tools and methods an engineer employs to produce sealed deliverables. Using an untested, unfamiliar tool on a live engagement — particularly one generating design documents that would bear a professional seal — without any prior validation, pilot testing, or independent verification protocol falls below the standard of care the Code demands. At minimum, before relying on AI-assisted drafting software for sealed deliverables, an engineer should: (1) conduct independent validation of the tool's outputs against known correct solutions; (2) understand the tool's known failure modes and limitations; and (3) establish a verification protocol sufficient to catch errors the tool is known to produce. Engineer A did none of these things for the design documents, and the resulting deficiencies — misaligned dimensions and omitted safety features — are a foreseeable consequence of that failure.
In response to Q103: Engineer A's omission of any disclosure about AI authorship, in the context of a report that Client W independently observed 'read as if written by two different authors,' constitutes a deceptive act or misrepresentation of authorship under Code Sections I.5 and III.3, independent of whether the factual content of the report was accurate. Deception under the Code does not require an affirmative false statement; it encompasses conduct that creates a false impression in the mind of the recipient. By submitting the report under their professional seal without any attribution of AI involvement, Engineer A implicitly represented the work as their own original professional writing. The stylistic inconsistency noticed by Client W is direct evidence that the AI's contribution was perceptible and material — not merely a background tool analogous to spell-check or grammar software. When a client can detect that a work product appears to have multiple authors, and the engineer withholds the explanation that one of those apparent 'authors' is an AI system, the omission crosses from permissible non-disclosure into functional misrepresentation. This conclusion is independent of the Board's finding that AI-assisted drafting is not unethical per se; the ethical problem here is not the use of AI but the concealment of its perceptible and substantial role.
In response to Q104: Engineer A's loss of Engineer B as mentor and quality assurance reviewer created a foreseeable and material competence gap that Engineer A was ethically obligated to address through means other than the unilateral adoption of an unfamiliar AI tool. The Code's competence obligations under Sections I.2 and II.2.a do not permit an engineer to substitute an untested technology for the human oversight and peer review that previously ensured the adequacy of their work product. Engineer A had several ethically permissible alternatives: engaging a substitute peer reviewer with relevant expertise, limiting the scope of accepted work to deliverables within Engineer A's independent capacity, or declining the engagement pending identification of adequate quality assurance support. Instead, Engineer A accepted both a complex technical report and engineering design documents — deliverables that would bear a professional seal — while simultaneously losing their primary quality assurance resource and adopting an unfamiliar AI tool as a replacement. This compounded the risk rather than mitigating it. The design deficiencies that resulted — misaligned dimensions and omitted safety features — are a direct and foreseeable consequence of this failure to address the competence gap through appropriate professional means.
In response to Q201: A genuine and irresolvable tension exists between the principle of technical accuracy achieved through thorough review and the principle of honest representation of authorship and process. The Board's finding that Engineer A's thorough cross-checking of the report satisfied the competence obligation addresses only the output quality dimension of the ethical analysis. It does not resolve — and in fact obscures — the separate question of whether Engineer A misrepresented the nature and origin of the work product. An engineer may produce a technically accurate report while simultaneously creating a false impression about who or what authored it. When these two obligations cannot be simultaneously fulfilled — that is, when using AI produces a technically adequate product but concealing AI use constitutes deception — the obligation of candor and non-deception should take precedence, because the Code's prohibitions on deceptive acts under Sections I.5 and III.3 are categorical in character and do not yield to consequentialist justifications based on output quality. Technical accuracy is a necessary but not sufficient condition for ethical compliance.
In response to Q203: A fundamental conflict exists between the act of affixing a professional seal and the adequacy of the oversight that seal is intended to certify. The professional seal is not merely a formality; under Code Section II.2.b and state engineering seal law, it represents the engineer's certification that they exercised responsible charge over the work. Engineer A's cursory review of AI-generated design documents — which failed to detect misaligned dimensions and omitted safety features required by local regulations — means that the seal was affixed to documents that Engineer A had not adequately reviewed. The seal therefore functioned not as a certification of responsible charge but as a misrepresentation of it. This is among the most serious ethical failures in the case, because the seal is the primary mechanism by which the public and clients rely on the engineer's professional judgment. To prevent this failure mode, procedural safeguards for sealing AI-generated design documents should include: (1) a systematic, element-by-element verification of all dimensions and specifications against source data; (2) explicit cross-checking of all design features against applicable local, state, and federal regulatory requirements; and (3) documented evidence of the review process sufficient to demonstrate responsible charge. A cursory review is categorically insufficient when AI has generated the primary content of sealed design documents.
In response to Q301 and Q302: From a deontological perspective, Engineer A failed two categorical duties simultaneously. First, the duty of candor to Client W was breached by omitting disclosure of AI involvement in both the report and the design documents. A Kantian analysis requires asking whether the maxim 'engineers may omit disclosure of AI authorship when the output is technically adequate' could be universalized without contradiction. It cannot: if all engineers concealed AI involvement whenever they judged the output satisfactory, clients would be systematically deprived of information material to their ability to evaluate the work product and the professional relationship, undermining the very trust on which professional licensure depends. Second, the duty to protect client confidentiality was breached by uploading Client W's proprietary data to an open-source platform without consent. This duty is categorical under Code Section II.1.c and does not admit of exceptions based on the engineer's assessment of likely harm. The absence of a confirmed data breach is irrelevant to the deontological analysis: the wrong consists in the unauthorized act of disclosure itself, not in its consequences. Both violations are independently sufficient to render Engineer A's conduct unethical under a deontological framework, and they compound each other in a way that reflects a broader pattern of prioritizing personal convenience over professional obligation.
In response to Q303: From a consequentialist perspective, the aggregate outcomes of Engineer A's AI-assisted workflow produce a net harm that substantially outweighs the efficiency gains sought. The benefits were limited to time savings in drafting and the ability to meet deliverable deadlines without a quality assurance reviewer. The harms include: (1) exposure of Client W's confidential site and groundwater data to an uncontrolled open-source platform, creating ongoing and unquantifiable data security risk; (2) submission of design documents containing misaligned dimensions and omitted safety features required by local regulations, creating direct public safety risk and requiring costly revision; (3) erosion of Client W's trust, evidenced by the client's observation that the report appeared to have two authors and by the client's instruction to revise the design documents; and (4) the broader systemic harm of normalizing inadequate AI oversight in professional engineering practice. The efficiency gain from AI adoption is only ethically defensible when the engineer has sufficient familiarity with the tool to ensure that the output meets professional standards. Here, Engineer A's unfamiliarity with the tool, combined with a cursory review of the design documents, meant that the efficiency gain was achieved by transferring risk to the client and the public rather than by genuinely improving productivity.
In response to Q304 and Q305: From a virtue ethics perspective, Engineer A's conduct in both the report and design document contexts falls short of the professional integrity and responsible stewardship that the public and clients are entitled to expect from a licensed engineer. A virtuous engineer, confronted with the loss of a trusted mentor and quality assurance reviewer, would respond by seeking alternative qualified oversight — not by substituting an unfamiliar, unvalidated AI tool for human professional judgment. The choice to adopt an open-source AI drafting tool with no prior experience, on a live client engagement involving sealed deliverables, reflects a disposition toward self-reliance that is admirable in principle but reckless in execution when the engineer lacks the competence to validate the tool's outputs. More seriously, Engineer A's decision to affix a professional seal to AI-generated design documents after only a cursory review reflects a self-serving shortcut that undermines the moral foundation of professional licensure. The seal exists to assure the public that a qualified professional has exercised genuine judgment over the work. Affixing it to documents that Engineer A had not adequately reviewed — and that contained detectable errors — is not merely a procedural lapse; it is a betrayal of the public trust that the seal is designed to embody. A virtuous engineer would have either declined to seal the documents until a thorough review was complete or sought qualified assistance before submission.
In response to Q306: A universal norm requiring engineers to disclose AI involvement whenever AI plays a substantial role in generating a work product would produce better outcomes for public safety, client trust, and the engineering profession than the current discretionary approach endorsed by the Board. The Board's analogy between AI tools and conventional engineering software — such as CAD or structural analysis programs — is imperfect in a critical respect: conventional engineering software executes deterministic calculations based on engineer-defined inputs, while large language model AI generates probabilistic text and design content that may contain errors, hallucinations, or stylistic artifacts that are not traceable to any specific engineer input. This fundamental difference in the nature of the tool's contribution justifies a different disclosure standard. Mandatory disclosure of substantial AI involvement would: (1) enable clients to make informed decisions about the work product and the professional relationship; (2) create accountability incentives that encourage engineers to conduct more rigorous reviews of AI-generated content; (3) allow the profession to develop empirical data about AI tool reliability across different application domains; and (4) prevent the kind of functional deception illustrated by this case, where a client independently detected AI involvement through stylistic inconsistency. The current discretionary approach, by contrast, permits concealment of AI involvement as long as the engineer judges the output to be adequate — a standard that is self-assessed, unverifiable, and inconsistent with the Code's broader commitment to transparency and public trust.
In response to Q401: Prior disclosure to Client W and explicit written consent for uploading confidential site data to an open-source AI platform would have resolved the confidentiality breach under Code Section II.1.c and would have substantially altered the ethical assessment of the engagement. Had Engineer A disclosed the intended use of open-source AI software before beginning work — including the fact that confidential client data would be input into the platform — Client W would have had the opportunity to evaluate the data security implications, negotiate alternative arrangements, or withhold consent. This would have either eliminated the confidentiality violation (if consent were granted) or prevented the data exposure entirely (if consent were withheld). However, prior consent for data upload would not have resolved all ethical violations: the design document deficiencies arising from inadequate review and the responsible charge failures would have persisted regardless of disclosure. The counterfactual therefore supports the conclusion that disclosure and consent are necessary but not sufficient conditions for ethical compliance — they address the confidentiality and transparency dimensions of the analysis but do not substitute for the competence and responsible charge obligations that Engineer A independently failed to satisfy.
In response to Q402: Had Engineer A engaged a qualified substitute reviewer to replace Engineer B's mentorship and quality assurance role before submitting either the report or the design documents, the design deficiencies, regulatory omissions, and responsible charge failures identified in this case would very likely have been prevented. A competent peer reviewer would have been expected to detect misaligned dimensions and omitted safety features — errors that Client W, a non-engineer client, was able to identify upon review. The presence of qualified peer review would also have satisfied the spirit of the responsible charge obligation under Code Section II.2.b, because the engineer's judgment would have been supplemented — rather than replaced — by another qualified professional. Under those circumstances, AI use would have remained ethically permissible: the tool would have served its appropriate function as a drafting aid subject to meaningful professional oversight, rather than as a substitute for the professional judgment that Engineer A lacked the independent capacity to fully exercise. This counterfactual reinforces the conclusion that the ethical failures in this case are not primarily attributable to AI use per se, but to the absence of adequate professional oversight — a gap that Engineer A was obligated to fill through qualified human review rather than through technological substitution.
In response to Q403: If Engineer A had applied the same level of thorough, cross-referenced review to the AI-generated design documents as they applied to the AI-generated report — verifying all dimensions against source data, checking safety feature requirements against local regulations, and reconciling site-specific conditions — the Board's finding of an ethical violation for failure to maintain responsible charge over the design documents would not have been warranted on review-adequacy grounds. The Board's distinction between the report (ethical) and the design documents (unethical) rests substantially on the difference in review quality: thorough review for the report, cursory review for the design documents. A thorough review of the design documents that successfully identified and corrected all misaligned dimensions and omitted safety features before submission would have satisfied the responsible charge obligation under Code Section II.2.b. However, this counterfactual does not eliminate all ethical concerns: the confidentiality breach from uploading client data to an open-source platform and the non-disclosure of AI involvement would have persisted regardless of review quality. The counterfactual therefore isolates the responsible charge violation as a curable deficiency — one that adequate review could have remedied — while confirming that the confidentiality and transparency violations are structural features of Engineer A's approach that no amount of post-generation review can retroactively cure.
In response to Q404: Voluntary disclosure of AI tool use in both the report and the design documents at the time of submission would have materially altered the client's trust, the professional relationship, and the Board's ethical assessment regarding the disclosure obligation. Client W's observation that the report 'read as if written by two different authors' demonstrates that the absence of disclosure was already functionally deceptive: the client perceived an anomaly that had a specific explanation — AI authorship of the introduction — that Engineer A withheld. Had Engineer A proactively disclosed the AI tools used and the extent of AI involvement, Client W would have had a factual explanation for the stylistic inconsistency, would have been positioned to make an informed judgment about the adequacy of Engineer A's review, and would have retained the ability to request additional assurances or alternative approaches. Such disclosure would have aligned Engineer A's conduct with the Code's requirements of candor under Sections I.5 and III.3 and would have transformed the AI use from a concealed practice into a transparent professional choice subject to client evaluation. The Board's conclusion that disclosure is not ethically required as a general matter does not address the specific circumstance where non-disclosure is already functionally deceptive — a circumstance this case presents directly. In that specific circumstance, the ethical obligation to avoid deception independently requires disclosure, regardless of whether a general disclosure norm exists.
Similar to other software used in the design or detailing process, Engineer A has no professional or ethical obligation to disclose AI use to Client W (unless such disclosure is required under Engineer A’s contract with Client W).
The most consequential unresolved tension in this case is between Engineer A AI Report Competence and Engineer A AI Authorship Concealment. The Board found that Engineer A's thorough cross-checking of the report satisfied the competence standard, effectively decoupling technical accuracy from the question of authorship transparency. However, this resolution is incomplete: an engineer can produce a factually correct work product while simultaneously misrepresenting—by omission—the nature and origin of that product. Client W's observation that the report read as if written by two different authors demonstrates that the concealment was not merely theoretical; it was perceptible and materially affected the client's understanding of what they received. The Board's implicit prioritization of output quality over process transparency teaches that, under the current framework, competence obligations can be satisfied independently of candor obligations. This case suggests that framework is insufficient: where AI plays a substantial authorship role and that role is perceptible to the client, the principle of Engineer A AI Disclosure Transparency should be treated as a corollary of competence rather than a separate, discretionary consideration. Competence and candor should be jointly necessary conditions for ethical submission of a sealed work product.
The tension between Engineer A Responsible Charge Design Failure and Engineer A Seal on AI Design Documents exposes a structural vulnerability in how professional sealing norms interact with AI-generated outputs. The professional seal is the legal and ethical mechanism by which an engineer certifies responsible charge—meaning the engineer has exercised sufficient judgment, direction, and verification over the work product to stand behind it publicly. In this case, Engineer A's cursory review of AI-generated design documents that contained misaligned dimensions and omitted safety features required by local regulations means the seal was affixed to a product over which responsible charge had not, in fact, been exercised. The seal did not confirm oversight; it obscured its absence. This tension was not resolved by the Board so much as it was exposed: the Board's finding of an ethical violation for the design documents implicitly acknowledges that sealing AI-generated work after only a high-level review fails the responsible charge standard. The case teaches that the principle of Engineer A Seal on AI Design Documents cannot be treated as equivalent to sealing conventionally produced documents unless the engineer's review is substantively equivalent in depth and rigor to what would have been required had the engineer produced the documents without AI assistance. The seal's moral and legal weight demands that AI-assisted design receive verification proportionate to the risk of the output, not merely a review proportionate to the engineer's familiarity with the tool.
The interaction among Engineer A Client Data AI Upload, Engineer A Professional Competence AI Use, and Engineer A Mentor Loss Response reveals that what appears on the surface to be a single decision—adopting an AI tool—was in fact a compounded ethical failure involving three independently assessable breaches that reinforced one another. First, uploading Client W's confidential site data and groundwater monitoring information into an open-source AI platform without prior consent violated the client confidentiality principle regardless of whether any data breach materialized, because the obligation under Code Section II.1.c is triggered by unauthorized disclosure, not by demonstrated harm. Second, adopting an AI drafting tool that was entirely new to the market with no prior experience, and applying it to sealed deliverables for a live client engagement, violated the competence principle because Engineer A could not evaluate the tool's accuracy, limitations, or failure modes before relying on it. Third, Engineer A's response to the loss of Engineer B as mentor and quality assurance reviewer—substituting an unfamiliar AI tool rather than seeking a qualified peer reviewer, limiting scope, or declining work beyond independent capacity—violated the principle of Engineer A Mentor Loss Response, which required Engineer A to address the competence gap through means that preserved rather than compounded professional risk. The case teaches that when multiple principles are simultaneously in tension, the ethical analysis must treat each breach as independently actionable rather than allowing a finding of compliance on one dimension—such as the quality of the report review—to offset or absorb violations on others. Engineer A AI Tool Non-Disclosure Report and Engineer A AI Disclosure Transparency cannot be resolved by analogy to conventional software tools when the tool in question is unfamiliar, open-source, and processing confidential client data, because that analogy assumes a baseline of tool competence and data security that was absent here.
Should Engineer A (and Engineer B as mentor) proactively disclose to Client W that AI tools were used to generate portions of the report and design, or treat AI as an internal drafting tool requiring no separate client notification?
Engineer B Mentor Engineer and Engineer A failed to disclose to Client W that AI tools were used in preparing the environmental report and design documents. The board must determine whether engineers are obligated to proactively disclose AI tool usage and AI-generated content to the client before submission, or whether existing professional review and sealing practices are sufficient.
Proactively inform Client W that AI tools were used to generate portions of the report and design, identify which sections were AI-assisted, and describe the verification steps taken before sealing.
Treat AI as an internal production tool equivalent to CAD or spreadsheet software, relying on the engineer's professional seal to represent full responsibility for the work product without separate disclosure of the tools used to produce it.
Document AI tool usage in the internal project file and quality assurance records without providing a separate client-facing disclosure, on the basis that tool-level documentation satisfies professional record-keeping obligations without requiring client notification.
Should Engineer B limit involvement to a cursory review of Engineer A's AI-generated design documents, or must Engineer B provide substantive mentorship and technical oversight sufficient to close the competence gap and ensure regulatory safety compliance?
Engineer B, acting as mentor, faces a decision about the adequacy of oversight provided to Engineer A on a project where AI tools are being used extensively and Engineer A has limited competence in AI-assisted design. The question is whether Engineer B's cursory review of design documents satisfies the mentorship and regulatory safety compliance obligations.
Conduct a substantive, targeted review of the AI-generated design documents focused specifically on the areas where Engineer A has demonstrated basic proficiency, including AI output verification and safety feature compliance, providing direct corrective guidance before Engineer A seals the documents.
Perform a high-level review of the design documents for obvious errors and general conformance, treating the mentorship role as advisory and leaving detailed technical verification to Engineer A as the responsible sealing engineer.
Formally notify project leadership that Engineer A's proficiency in AI-assisted design is insufficient for the scope of work without additional support, recommend reassignment of design responsibility or engagement of a specialist, and decline to provide a review that would imply adequate oversight has been achieved.
Should Engineer A conduct a full independent technical review of AI-generated design documents before sealing them, or apply standard QA protocols treating AI output as equivalent to conventional drafting tools?
Engineer A sealed design documents produced largely by AI without exercising responsible charge, omitting required safety features. The core question is whether Engineer A should have conducted a thorough independent technical review before sealing, or whether applying standard QA protocols to AI-generated design outputs was sufficient.
Perform a complete, line-by-line independent technical review of all AI-generated design documents, verifying safety features, code compliance, and design integrity before sealing, regardless of time or resource constraints.
Apply the same QA review process used for conventionally drafted documents, treating AI output as equivalent to CAD-generated drawings and relying on standard checklist-based review before sealing.
Conduct a targeted independent review of safety-critical design elements with a qualified peer reviewer while applying standard QA protocols to non-safety-critical portions, acknowledging the limits of solo AI output verification.
Should Engineer A fully disclose AI tool usage to the client and apply heightened verification to AI-generated report and design outputs, or treat AI as an internal drafting tool requiring only standard review and no separate disclosure?
Engineer A used AI tools to generate both a technical report and design documents without disclosing AI involvement to the client or mentor, and without adequately verifying AI-generated outputs. The question is whether Engineer A had an obligation to disclose AI tool usage and verify outputs to a higher standard given the circumstances.
Proactively inform the client that AI tools were used in producing the report and design documents, identify AI-generated sections, and apply a more rigorous verification standard to AI outputs than would be applied to conventionally produced work.
Use AI tools as internal productivity aids equivalent to CAD or spreadsheet software, applying standard QA review without separate client disclosure, on the basis that the engineer remains professionally responsible for the final sealed product.
Note AI tool usage in project documentation and methodology sections of the report without a separate client notification, following existing firm software disclosure policy and relying on the client to inquire further if desired.
Should Engineer A seek alternative mentorship or supervisory support after losing access to a mentor, or continue the project independently given existing competence in the domain?
Engineer A lost access to a mentor mid-project and continued work without seeking alternative supervision or guidance, despite having only basic proficiency in several critical areas including responsible charge verification and AI direction control for design. The question is whether Engineer A should have sought alternative mentorship or paused work rather than proceeding independently.
Pause work on tasks requiring responsible charge and AI oversight, and actively seek a qualified supervising engineer or peer reviewer before proceeding, acknowledging the gap created by the mentor's departure.
Continue project work limited strictly to tasks within Engineer A's demonstrated competence, deferring or flagging tasks requiring higher-level oversight until a replacement mentor or supervisor is identified.
Continue the full scope of project work independently, relying on AI tools to supplement the guidance previously provided by the mentor, on the basis that advanced domain competence and AI assistance together constitute adequate professional judgment.
Should Engineer A proactively disclose AI tool usage to the client and conduct independent verification of all AI-generated design and report content before sealing and submitting deliverables, or is it sufficient to apply standard QA protocols without separate disclosure?
Engineer A used an AI tool to generate substantial portions of both the design documents and the project report without disclosing this to the client, without maintaining adequate direction and control over AI outputs in the design phase, and without verifying AI-generated design content before sealing. Multiple obligations related to disclosure, responsible charge, and design verification remain unmet. The core tension is whether Engineer A must proactively disclose AI involvement and conduct independent verification before sealing and submitting deliverables, or whether existing QA practices and professional judgment are sufficient.
Proactively inform the client of AI tool involvement in both the design and report deliverables, conduct independent technical verification of all AI-generated content before sealing, and correct any safety or technical deficiencies identified during review.
Treat the AI tool as equivalent to conventional engineering software such as CAD or finite element analysis programs, applying the firm's standard quality assurance protocols without issuing a separate disclosure to the client, on the basis that method selection is an internal professional judgment.
Acknowledge AI tool usage within the project report's methodology section and design document notes without a separate client notification, and apply enhanced verification only to safety-critical design elements while relying on domain expertise for remaining outputs.
Should Engineer A refrain from uploading Client W's confidential site and groundwater data into open-source AI platforms, or is use of such platforms permissible when the engineer exercises professional judgment over the outputs?
Engineer A uploaded Client W's confidential site data and groundwater monitoring information into open-source AI software, exposing that data to a third-party platform outside the protected professional relationship. The board must determine whether this action violated the engineer's confidentiality obligation to the client and what standard of care applies when using AI tools that process client data.
Decline to input Client W's confidential site and groundwater data into any open-source AI platform, instead using AI tools only with anonymized or non-confidential data, or using enterprise AI platforms with contractual data protection agreements.
Seek explicit written authorization from Client W before uploading any confidential project data to AI platforms, disclosing the platform's data handling practices and allowing the client to make an informed decision about acceptable risk.
Treat AI platforms as equivalent to other cloud-based engineering software routinely used without specific client authorization, relying on the engineer's professional judgment that the efficiency and quality benefits justify use of available tools within normal practice.
Must Engineer A verify, independently direct, and properly attribute all AI-generated technical content in sealed reports and design documents, or is applying standard QA review to AI outputs sufficient to satisfy responsible charge obligations?
Engineer A submitted a report and design documents that omitted citations to AI-generated technical content and misrepresented the authorship of the work product. Multiple obligations related to technical citation, authorship representation, responsible charge, and design verification are marked unmet. The board must determine what standard of verification and attribution applies when AI tools generate substantive technical content that an engineer then seals.
Independently verify all AI-generated technical content against engineering first principles or established references, document the verification process, include proper citations to AI tools and source data, and seal only content that the engineer can personally defend as technically sound.
Apply the same quality assurance review procedures used for work produced by junior engineers or conventional software to AI-generated content, treating the engineer's professional judgment in reviewing outputs as sufficient to establish responsible charge without requiring independent re-derivation of every result.
Apply heightened independent review to AI-generated content in safety-critical design elements while applying standard QA protocols to non-safety-critical sections, and defer sealing until a senior engineer with AI competency reviews the safety-critical outputs.
Should Engineer A proactively disclose to the client that AI tools generated substantial portions of the design and report, or present the deliverables as conventionally authored engineering work?
Engineer A must decide whether to disclose to the client that AI tools generated substantial portions of both the design documents and the written report, or to present the work product as conventionally authored engineering output.
Inform the client before or upon delivery that AI tools generated substantial portions of the design documents and report, identifying which sections were AI-produced and describing the verification steps taken.
Treat AI tools as equivalent to other computational or drafting software such as CAD or FEA packages, disclosing AI usage only if the client specifically inquires, consistent with how other software tools are handled.
Document AI tool usage in internal project records and deliverable metadata without a separate client notification, on the basis that the engineer reviewed and sealed the work and therefore bears full professional responsibility regardless of the generative method.
Should Engineer B require Engineer A to disclose AI authorship of the deliverables to the client, or accept the work as submitted on the basis that Engineer B's review satisfies the professional responsibility?
Engineer B, acting as mentor to Engineer A, must decide whether to require Engineer A to disclose the AI-generated authorship of the report and design to the client, or to allow the work to proceed without that disclosure given that Engineer B reviewed the output.
Direct Engineer A to notify the client of AI tool usage in the design and report before final delivery, and confirm that disclosure has been made before Engineer B provides any further mentorship endorsement of the work.
Conduct an independent technical review of the AI-generated content and, if satisfied with quality and accuracy, allow the deliverables to proceed without requiring separate AI disclosure, on the basis that Engineer B's review establishes professional accountability for the output.
Counsel Engineer A on the professional risks of omitting AI disclosure and recommend disclosure as best practice, but ultimately defer to Engineer A's judgment as the engineer of record rather than mandating a specific course of action.
Should Engineer A seal the AI-generated design documents based on the review conducted, or withhold the seal until a more rigorous independent verification of safety compliance and responsible charge is completed?
Engineer A must decide whether to seal design documents that were substantially generated by AI tools without adequate verification of safety compliance and responsible charge, or to withhold the seal until independent verification is complete.
Decline to seal the design documents until a complete independent technical review confirms safety compliance and all required design features are present, accepting schedule delay as necessary to meet responsible charge obligations.
Apply the firm's standard quality assurance review process to the AI-generated design, treating AI output as equivalent to conventionally drafted documents and sealing upon completion of that review, consistent with how other computer-assisted design tools are handled.
Retain an independent reviewer with relevant safety expertise to evaluate the safety-critical portions of the AI-generated design before sealing, while applying standard review to non-safety-critical elements, balancing schedule pressure against the heightened risk of AI-generated safety features.
Should Engineer B conduct a full independent technical review of the AI-assisted design documents before sealing, or apply a standard supervisory review comparable to reviewing conventional CAD-produced work?
Engineer B must decide what level of review to apply to design documents that Engineer A produced with substantial AI assistance, before affixing a seal as the responsible charge engineer.
Review every substantive element of the AI-assisted design documents independently, verifying calculations, safety features, and code compliance before sealing, treating AI-generated content as requiring the same scrutiny as work from an unfamiliar subconsultant.
Apply the firm's existing quality assurance protocols to the AI-assisted documents at the same level used for conventional CAD-produced work, relying on Engineer A's advanced environmental domain competence and intermediate verification proficiency as a reasonable internal check.
Commission a focused third-party technical review of safety-critical and AI-generated design elements while applying standard supervisory review to the remaining portions, balancing thoroughness with practical resource constraints.
Should Engineer A proactively disclose to the client and to Engineer B the nature and extent of AI tool usage in the design and report before those documents are finalized, or treat the AI tools as internal drafting aids that do not require separate disclosure?
Engineer A must decide how to disclose the AI tools used and the extent of AI involvement in both the design documents and the report, given that multiple AI disclosure and authorship obligations are unmet.
Proactively inform the client and Engineer B of the specific AI tools used, the portions of the design and report they generated, and the verification steps taken, before any documents are sealed or submitted.
Treat AI tools as internal drafting aids equivalent to spreadsheet or CAD software, disclosing their use only if the client or Engineer B specifically asks, on the basis that professional responsibility attaches to the engineer's judgment rather than to the tools employed.
Record the AI tools and their outputs in the internal project file to preserve a verifiable audit trail, while omitting proactive client notification on the grounds that the engineer's professional seal represents adequate assumption of responsibility for all methods used.
Should Engineer A seal and submit the AI-generated design documents after only a cursory review, or must Engineer A conduct a thorough independent technical verification before sealing?
Engineer A faces a decision about whether to seal and submit design documents that were substantially generated by AI tools without adequate independent technical verification, responsible charge oversight, or confirmation that required safety features are present.
Perform a comprehensive, line-by-line technical review of all AI-generated design documents, independently verify that all required safety features are present and code-compliant, and only affix the professional seal after establishing genuine responsible charge over the entire design.
Apply the firm's standard quality assurance review protocols to the AI-generated documents at the same level used for conventionally drafted designs, treating AI as an advanced drafting tool, and seal upon completion of that standard review without a separate AI-specific verification layer.
Pause the sealing process, engage a qualified senior engineer or external reviewer to provide the mentorship and technical oversight lost when Engineer B departed, and seal only after that substitute oversight confirms the design documents are complete, safe, and accurate.
Engineer A's site observation and data gathering before project deliverables due date
groundwater monitoring data from a site Engineer A had been observing for over a year
Engineer A's site observation and data gathering
Hold paramount the safety, health, and welfare of the public.
Perform services only in areas of their competence.
Engineers shall not reveal facts, data, or information without the prior consent of the client or employer except as authorized or required by law or this Code.
Engineers shall undertake assignments only when qualified by education or experience in the specific technical fields involved.
Engineers shall not affix their signatures to any plans or documents dealing with subject matter in which they lack competence, nor to any plan or document not prepared under their direction and control.
Engineers shall avoid all conduct or practice that deceives the public.
Engineers shall conform with state registration laws in the practice of engineering.
Engineers shall give credit for engineering work to those to whom credit is due, and will recognize the proprietary interests of others.
This question arose because the act of uploading client data to an open-source AI platform is a factual trigger that sits at the intersection of two legitimate professional obligations, one demanding confidentiality and one demanding competent direction of tools. The deontological framing sharpens the question by asking whether the confidentiality duty is categorical and outcome-independent, which forces a determination of whether the absence of an actual breach is legally or ethically irrelevant to the duty itself.
This question arose because Engineer B's retirement created a foreseeable structural change in Engineer A's quality assurance capacity at the same moment Engineer A adopted an unfamiliar AI tool and accepted sealed deliverables, producing a compound competence deficit that the responsible charge obligation alone may not have been designed to address. The question forces analysis of whether the obligation to practice within competence required Engineer A to act before the engagement rather than only during it, and what specific remediation steps that obligation demanded.
This question emerged because the Board's competence finding and the concealment finding were reached independently, leaving open whether satisfying one obligation can offset a violation of the other. The two principles address different dimensions of professional conduct, technical quality and representational honesty, and the case provides no hierarchy for resolving their conflict when a single submission implicates both.
The question emerged because Engineer A's conduct simultaneously satisfied the factual conditions that activate both warrants: the AI contribution was substantial enough that stylistic inconsistency became detectable, yet the Board's own reasoning acknowledged the software analogy as a legitimate basis for non-disclosure. The absence of a governing regulatory standard left both warrants in play without a rule to subordinate one to the other, making the tension a genuine unresolved conflict rather than a clear violation.
This question emerged because the professional seal is designed to function as a guarantee of responsible charge, but Engineer A's cursory review of AI-generated plans containing misaligned dimensions and omitted safety features meant the seal communicated a level of oversight that had not actually occurred. The gap between what the seal formally represents and what Engineer A actually performed created a direct conflict between the obligation to seal work under responsible charge and the obligation to ensure that responsible charge was genuinely exercised before sealing.
This question emerged because Engineer A's single act of uploading confidential client data to an unfamiliar AI tool simultaneously activated two distinct ethical warrants, one protecting client privacy and one requiring professional competence, and the factual record does not resolve whether those warrants operate on the same normative ground or on independent grounds. The question of whether to treat the violations as compounded or independent is not merely taxonomic but determines how responsibility is assigned, what remedies are appropriate, and whether the ethical analysis focuses on harm to the client, harm to professional standards, or both.
This question emerged because Engineer B's retirement created a genuine competence gap at the same moment Engineer A faced dual deliverable pressure, and Engineer A responded by deploying an unfamiliar AI drafting tool rather than seeking alternative qualified oversight or declining work beyond independent capacity. The virtue ethics framing sharpens the question by asking not just whether the outcome was harmful but whether Engineer A demonstrated the self-knowledge and professional honesty that integrity requires before accepting and executing work under those conditions.
This question arose because the professional seal is the institutional symbol of responsible stewardship, and Engineer A's cursory review created a direct contest between the virtue of diligence that the seal represents and the self-serving convenience of accepting AI output without the verification that would make the seal honest. The discovery of design deficiencies after submission made the gap between the character trait the seal implies and the character trait Engineer A actually demonstrated impossible to ignore.
This question emerged because the actual harms produced by Engineer A's undisclosed AI use, including design defects, public safety risks, and client data exposure, created observable evidence that discretionary disclosure failed to protect the interests it was meant to protect. The gap between the outcomes the current approach produced and the outcomes a universal norm might have produced forced a consequentialist evaluation of whether the warrant authorizing discretion is strong enough to survive the rebuttal that mandatory disclosure would have prevented those harms.
This question arose because the Board identified multiple distinct violations, and it is genuinely unclear whether a single procedural act, prior written consent, would have collapsed those violations into a compliant engagement or whether the responsible charge, verification, and public safety failures would have remained as independent grounds for ethical censure. The question forces a structural analysis of whether the violations are causally linked to the absence of consent or whether they reflect deeper competence and oversight failures that consent alone could not have cured.
This question arose because the Board's findings identified failures across multiple dimensions simultaneously, including design deficiencies, regulatory omissions, and responsible charge deficits, and it became unclear whether those failures were caused by the absence of a substitute reviewer specifically or by a broader pattern of inadequate verification that a substitute reviewer might not have corrected. The question forces a structural analysis of whether the ethical permissibility of AI use depends on the presence of a qualified human reviewer in the oversight chain, or whether it depends on the competence and rigor of whoever holds responsible charge.
This question arose because Engineer A's AI-assisted workflow produced multiple distinct categories of harm across a single project, and consequentialism demands a unified net assessment rather than isolated evaluation of each failure. The question became necessary because the data shows real efficiency motivation alongside real harm production, and no single warrant resolves whether the aggregate outcome is net negative without contested empirical judgments about harm probability, severity, and the counterfactual value of the efficiency gains.
This question emerged because the Board's finding rested on the inadequacy of Engineer A's review of the design documents, but Engineer A had demonstrated the capacity for thorough AI output review in the context of the report. The question asks whether the ethical violation was a function of the review process Engineer A chose to apply, rather than an inherent consequence of using AI-assisted drafting tools, which creates genuine uncertainty about whether the violation was avoidable through conduct rather than unavoidable through context.
This question arose because the stylistic inconsistency noticed by Client W created observable evidence that the work product had a non-uniform origin, which transformed what might have been a private tool-use decision into a situation where the client was already drawing inferences about authorship without accurate information. The question then asks whether voluntary disclosure would have reframed the entire ethical situation, because if transparency at submission would have resolved the client's concern and altered the Board's analysis, then the omission itself becomes the operative ethical failure rather than the AI use.
This question emerged because Engineer A sealed and submitted AI-generated work products to Client W without any acknowledgment of AI involvement in either deliverable, creating a factual record of non-disclosure that directly contests the deontological principle that candor to a client is an obligation owed regardless of consequences. The question is not resolved by pointing to outcome because deontological analysis asks whether the duty was honored at the moment of action, and the competing warrants disagree on whether non-disclosure of AI authorship constitutes a breach of that duty or merely a lapse in attribution practice.
This question arose because the data simultaneously supports two defensible positions: that AI tools are legitimate engineering aids when an engineer seals and takes responsibility for the work, and that a high-level review of AI-generated design documents falls short of the direction and control required by responsible charge. The Engineer A Design Deficiencies state, combined with the Engineer A Unfamiliar Drafting Tool state and the loss of mentorship oversight captured in Engineer A Mentor Unavailable, created a factual record that makes it genuinely contestable whether Engineer A's review process met the standard that would make the AI tool use ethical.
The question emerged because Engineer A produced client deliverables substantially through AI tools and then omitted any mention of that fact, placing the client in a position of receiving work without knowing its origin. The absence of an explicit disclosure rule created genuine uncertainty about whether the obligation to be transparent about authorship and method extends beyond the engineer's internal professional responsibility and reaches the client relationship directly.
The question arose because Engineer A's thorough review satisfies the professional competence and responsible charge obligations that normally govern work product quality, but the act of submitting AI-generated text without disclosure leaves the attribution and intellectual honesty obligations unresolved. The two sets of obligations point to different conclusions about the same conduct, and neither fully overrides the other given the facts, which is precisely what generates the ethical question.
This question arose because Engineer A's decision to input Client W's confidential data into an open-source AI platform created a potential confidentiality violation that is analytically separate from any question about the report's technical adequacy. The question forces a determination of whether a procedural breach in how work was generated can independently condemn a submission that might otherwise be technically sound, which the Code's structure does not explicitly resolve.
The question emerged because Engineer A's state of unfamiliarity with the AI drafting tool, combined with the loss of mentor oversight after Supervisor Retirement and the pressure of Dual Deliverable Pressure, produced sealed design documents with safety-relevant deficiencies. The absence of any profession-wide threshold for AI tool proficiency before sealed use left the competence standard contested, forcing the question of what minimum readiness is required before an engineer may rely on such tools for deliverables that carry a professional seal.
This question arose because Client W's direct observation of stylistic inconsistency transformed what might otherwise be a silent omission into a situation where the client was actively forming an incorrect inference about authorship, making Engineer A's silence function more like a tacit false assertion. The question is whether the combination of a detectable anomaly and continued non-disclosure crosses the line from mere omission into misrepresentation under the cited code sections, independent of factual accuracy.
Was Engineer A’s use of AI to create the report text ethical, given that Engineer A thoroughly checked the report?
By uploading Client W's confidential site data and groundwater monitoring information into an open-source AI platform without obtaining Client W's prior consent, did Engineer A breach the client confidentiality obligation under Code Section II.1.c, and does that breach independently render the report submission unethical regardless of the quality of Engineer A's subsequent review?
Given that Engineer A used an AI drafting tool that was entirely new to the market and with which Engineer A had no prior experience, did Engineer A satisfy the competence requirement under Code Sections I.2 and II.2.a before adopting that tool for a live client engagement, and what minimum level of tool-specific proficiency should be required before an engineer may rely on AI-assisted drafting software for sealed deliverables?
Because the AI-generated report produced stylistically inconsistent text that Client W noticed and attributed to what appeared to be two different authors, did Engineer A's omission of any disclosure about AI authorship constitute a deceptive act or misrepresentation of authorship under Code Sections I.5 and III.3, independent of whether the factual content of the report was accurate?
To what extent did Engineer A's loss of Engineer B as a mentor and quality assurance reviewer create a foreseeable competence gap that Engineer A was obligated to address through means other than unilateral adoption of an unfamiliar AI tool—such as engaging a substitute peer reviewer, limiting the scope of accepted work, or declining the engagement—before undertaking sealed deliverables for Client W?
Was Engineer A’s use of AI-assisted drafting tools to create the engineering design documents ethical, given that Engineer A reviewed the design at a high level?
Does the principle of Engineer A AI Report Competence—which the Board found satisfied by Engineer A's thorough cross-checking of the report—conflict with the principle of Engineer A AI Authorship Concealment, in that an engineer may produce a technically accurate work product while simultaneously misrepresenting the nature and origin of that work product, and if so, which obligation should take precedence when the two cannot be simultaneously fulfilled?
Does the principle of Engineer A AI Disclosure Transparency—which the Board acknowledged is ethically favored when AI plays a substantial role—conflict with the principle of Engineer A AI Tool Non-Disclosure Report, which the Board grounded in an analogy to conventional engineering software that carries no mandatory disclosure obligation, and how should engineers resolve this tension in the absence of a universal regulatory standard?
Does the principle of Engineer A Responsible Charge Design Failure—arising from Engineer A's cursory review of AI-generated plans that contained misaligned dimensions and omitted safety features—conflict with the principle of Engineer A Seal on AI Design Documents, in that affixing a professional seal is intended to certify responsible charge but the act of sealing here obscured rather than confirmed the adequacy of engineering oversight, and what procedural safeguards should govern the sealing of AI-generated design documents?
Does the principle of Engineer A Client Data AI Upload—which reflects the engineer's interest in leveraging available tools to meet client deliverable demands—conflict with the principle of Engineer A Professional Competence AI Use, in that using an unfamiliar open-source AI tool to process confidential client data may simultaneously undermine both the client's data security interests and the engineer's own competence obligations, and should the ethical analysis treat these as a single compounded violation or as two independently assessable breaches?
If the use of AI was acceptable, did Engineer A have an ethical obligation to disclose the use of AI in any form to the Client?
From a deontological perspective, did Engineer A fulfill their duty of candor to Client W by omitting any disclosure of AI involvement in both the report and the engineering design documents, regardless of whether that omission ultimately harmed the client?
From a deontological perspective, did Engineer A violate a categorical duty to protect client confidentiality by uploading Client W's proprietary site data and groundwater monitoring information into an open-source AI platform without obtaining prior consent, irrespective of whether any actual data breach occurred?
From a consequentialist perspective, did the aggregate outcomes of Engineer A's AI-assisted workflow — including the polished but stylistically inconsistent report, the deficient design documents with misaligned dimensions and omitted safety features, and the potential exposure of confidential client data — produce a net harm that outweighs the efficiency gains Engineer A sought by adopting AI tools?
From a virtue ethics perspective, did Engineer A demonstrate the professional integrity and intellectual honesty expected of a licensed engineer by choosing to use an unfamiliar, open-source AI drafting tool — with no prior experience — as a substitute for the mentorship and quality assurance oversight previously provided by Engineer B, rather than seeking alternative qualified review or declining work beyond their current independent capacity?
From a virtue ethics perspective, does Engineer A's decision to apply their professional seal to AI-generated design documents after only a cursory review reflect the character trait of responsible stewardship that the public and clients are entitled to expect from a licensed professional engineer, or does it represent a self-serving shortcut that undermines the moral foundation of professional licensure?
From a consequentialist perspective, would a universal norm requiring engineers to disclose AI involvement whenever AI plays a substantial role in generating a work product produce better outcomes for public safety, client trust, and the engineering profession than the current discretionary approach endorsed by the Board?
If Engineer A had disclosed to Client W, prior to beginning work, that they intended to use open-source AI software and had obtained explicit written consent for uploading confidential site data to that platform, would the ethical violations identified by the Board regarding client data exposure and lack of attribution have been avoided, and would the overall ethical assessment of the engagement have changed?
What if Engineer A had sought a qualified substitute reviewer — such as a peer engineer or a licensed colleague — to replace Engineer B's mentorship and quality assurance role before submitting either the report or the design documents? Would the design deficiencies, regulatory omissions, and responsible charge failures identified by the Board have been prevented, and would AI use have remained ethically permissible under those circumstances?
If Engineer A had conducted the same level of thorough, cross-referenced review on the AI-generated design documents as they applied to the AI-generated report — verifying dimensions, checking safety feature requirements against local regulations, and reconciling site-specific conditions — would the Board's finding of an ethical violation for failure to maintain Responsible Charge over the design documents still have been warranted?
What if Engineer A had voluntarily disclosed the use of AI tools in both the report and the design documents — including the specific AI software used and the extent of AI involvement — at the time of submission to Client W? Would such proactive transparency have altered the client's trust, the professional relationship, or the Board's ethical assessment regarding the disclosure obligation, and does the client's observation that the report 'read as if written by two different authors' suggest that non-disclosure was already functionally deceptive?
Because Engineer A exposed Client W's confidential data to a system with unknown retention and access policies before obtaining any consent, the board found an independent breach of II.1.c that the quality of the resulting report could not retroactively cure, since the violation was complete at the moment of upload.
Because the Board found the report technically competent but simultaneously found the concealment of AI authorship perceptible and materially misleading, the Board concluded that the existing framework is insufficient and that competence and candor should be treated as jointly necessary conditions for ethical submission of a sealed work product where AI plays a substantial authorship role.
Because Engineer A reviewed the AI-generated design documents only at a high level and the documents contained misaligned dimensions and omitted required safety features, the board found that the act of sealing did not confirm responsible charge but instead obscured its absence, making the sealing and submission of those documents an ethical violation under the responsible charge standard.
Because Engineer A's single decision to adopt an AI tool compounded three distinct ethical failures that each independently violated a code obligation, the board concluded that the ethical analysis could not treat these as a unified or offsetting set of considerations, and that each breach remained actionable on its own terms regardless of Engineer A's performance on other dimensions of the engagement.
Because Engineer A thoroughly checked the AI-generated report and verified its technical content before submission, the board found that the use of AI for report drafting was not unethical in that respect, though the conclusion is partial because other aspects of the same engagement, including the data upload and the design documents, were found to be unethical.
Because the board found that AI-assisted drafting is not categorically different from other engineering software tools when the engineer exercises adequate oversight, it concluded that Engineer A's use of AI for drafting was not unethical per se, with the ethical assessment turning on the quality of the review performed rather than on the fact of AI use itself.
Because no contractual or regulatory requirement compelled disclosure and the board analogized AI drafting tools to conventional software, it concluded that Engineer A had no independent ethical obligation to disclose AI use to Client W, though this resolution is conditional on the absence of a disclosure requirement and does not address whether the client-observable stylistic inconsistency independently triggered a duty to correct a potentially deceptive impression.
Because Client W could detect a stylistic discontinuity consistent with multiple authors, and because Engineer A offered no explanation, the board found that the omission created a false impression of sole human authorship. The conclusion rests on the perceptibility of the AI contribution in this case, not on a universal rule that all AI use must be disclosed.
Because Engineer A simultaneously lost their mentor, accepted complex sealed deliverables, and adopted an unfamiliar AI tool without any substitute peer review, the board found that the competence gap was foreseeable and the resulting design deficiencies were a direct consequence of failing to address it through appropriate professional means.
Because Engineer A's thorough review addressed only output quality and left the authorship misrepresentation intact, the board found that satisfying the competence obligation does not discharge the separate and categorical obligation of candor, and that the latter must prevail when the two cannot be simultaneously fulfilled.
Because Engineer A's review did not detect errors that a systematic check would have caught, the board found that the seal functioned as a misrepresentation of responsible charge rather than a certification of it, and prescribed specific procedural safeguards to prevent this failure mode when AI generates sealed design content.
Because Engineer A had no experience with the AI tool and because the tool's probabilistic output characteristics created a verification burden that conventional software does not, the board concluded that adopting it for live sealed work without any tool-specific qualification failed the competence threshold under I.2 and II.2.a, regardless of whether AI tools are permissible in principle.
Because Engineer A acted without client consent on the data upload and without disclosure on the authorship question, the board found two independent categorical violations that compound each other as a pattern of prioritizing personal convenience over professional obligation, with the absence of a confirmed breach being irrelevant to the deontological analysis.
Because Engineer A was unfamiliar with the tool and reviewed the design documents only at a high level, the efficiency gains were achieved by offloading risk onto the client and the public rather than by genuinely improving productivity, and the board found that the aggregate harms across data security, public safety, client trust, and professional norms substantially outweighed those gains.
Because Engineer A chose technological substitution over qualified human oversight after losing Engineer B, and then sealed documents containing detectable errors following only a cursory review, the board found that both decisions reflected a disposition that fell short of the professional integrity and responsible stewardship that licensure demands.
Because AI language models produce content through probabilistic processes that differ fundamentally from deterministic engineering software, and because non-disclosure in this case was already functionally deceptive as evidenced by the client's independent detection of AI involvement, the board concluded that a universal disclosure norm for substantial AI involvement would produce better outcomes than the current discretionary standard.
Because Engineer A's violations included both the unauthorized data upload and the independent failures of competence and responsible charge over the design documents, the board found that prior consent would have eliminated the confidentiality breach but would have left the design deficiency and sealing violations intact.
Because the design deficiencies were detectable by a non-engineer client and arose from the absence of qualified oversight rather than from AI use per se, the board concluded that engaging a qualified substitute reviewer before submission would have prevented the responsible charge failures and preserved the ethical permissibility of AI-assisted drafting.
Given that the Board's distinction between the report and the design documents rested substantially on the difference in review depth rather than on the mere fact of AI use, the Board concluded that a counterfactually thorough review of the design documents would have satisfied the responsible charge obligation under II.2.b, because the deficiencies in the submitted documents were curable through the same verification process Engineer A successfully applied to the report. However, because the confidentiality breach and the non-disclosure of AI involvement were structural features of Engineer A's workflow that preceded and were independent of any review step, those violations would have persisted even under the counterfactual, confirming that responsible charge is a curable deficiency while confidentiality and transparency violations are not.
Because Client W could detect the stylistic discontinuity between AI-generated and human-written sections, Engineer A's silence about AI involvement functioned as an implicit claim of sole human authorship, placing the omission in tension with the deception prohibitions in I.5 and III.3 and the attribution obligation in III.9.
Because the design review was cursory rather than substantive, and because the sealed documents contained errors that a competent review would have caught, the board found that the seal misrepresented the degree of professional oversight applied and that this failure, given the public safety consequences of deficient design documents, was the most serious ethical violation in the case.
Because Engineer B's retirement removed a structural component of Engineer A's quality assurance process and because Engineer A's substitution of an unfamiliar AI tool for that human oversight was not a professionally adequate adaptation, the board concluded that the mentor loss elevated rather than relaxed Engineer A's obligations and that the ethical failures in this case were foreseeable consequences of failing to address that gap through appropriate professional measures.
Given that no mandatory disclosure rule currently exists and the Board analogized AI tools to conventional engineering software, the Board concluded that disclosure is ethically favored but not required, while simultaneously warning that this standard is unstable and will likely produce worse outcomes as AI capabilities grow.
Because Engineer A exposed Client W's confidential data to an uncontrolled platform without consent, the Board found an independent ethical breach under II.1.c, treating the absence of demonstrated harm as irrelevant to whether the obligation was violated.
Because Engineer A used an untested, unfamiliar tool on a live engagement without any prior validation or verification protocol, and because the resulting deficiencies were foreseeable, the Board concluded that the competence requirement under I.2 and II.2.a was not satisfied before the tool was adopted.
Because Client W could perceive the stylistic inconsistency caused by AI authorship and Engineer A withheld the explanation, the Board concluded that the omission crossed from mere non-disclosure into functional deception, independently triggering the candor obligations under I.5 and III.3 in this specific circumstance.
property=causedByAction; causal chain's causedByAction text resolved to the case individual(s) by embedding shortlist + LLM select
property=cause; causal chain's cause text resolved to the case individual(s) by embedding shortlist + LLM select
property=effect; causal chain's effect text resolved to the case individual(s) by embedding shortlist + LLM select
property=responsibleAgent; causal chain's responsibleAgent text resolved to the case individual(s) by embedding shortlist + LLM select
source_field=interpretation; confidence=0.7
Using a novel AI tool without understanding its accuracy limitations, and reviewing its output only cursorily, falls below the competence standard required before sealing and submitting engineering design documents.
source_field=interpretation; confidence=0.65
Responsible charge requires active engagement from conception to completion, not merely reviewing outputs after preparation. Engineer A's high-level review of AI-generated plans did not satisfy this standard, constituting a violation of licensure law and NSPE Code section III.8.a.
source_field=interpretation; confidence=0.5
The BER treated this as the most serious ethical concern arising from Engineer A's use of AI-assisted drafting, grounding the responsible charge and competence violations in the fundamental canon.
source_field=interpretation; confidence=0.6
The report review was more substantive than the design document review, but the inconsistency in writing style noted by the client suggests the verification did not fully address the limitations of AI-generated synthesis.
Because Engineer A had domain expertise in environmental engineering and conducted substantive verification of the AI output, the use of AI for report drafting was within the bounds of professional competence for this component of the work.
property=initiates; happening's initiates state text resolved to the case State(s) by embedding shortlist + LLM multi-select (Event Calculus fluent transition)
AI-Drafted Design Exists; Unverified Design Content State
Undisclosed AI Contribution to Design Documents State
Undisclosed AI Contribution to Report State
AI-Drafted Report Exists; Unverified Content State
AI-Assisted Design Document Drafting State; Client Confidential Data in Public Domain State
AI-Assisted Report Drafting State; Client Confidential Data in Public Domain State
Confidentiality Risk State; Third-Party Data Exposure State
property=terminates; happening's terminates state text resolved to the case State(s) by embedding shortlist + LLM multi-select (Event Calculus fluent transition)
Client Confidential Data in Public Domain State
Public Safety Risk State; Regulatory Noncompliance Risk State; Insufficient Oversight of Design Documents State
Public Safety Risk; Regulatory Non-Compliance State; Design Revision Required State; Client Confidence Impaired State
Deficient Design Documents Submitted State; Public Safety Risk State; Regulatory Noncompliance Risk State
property=guidedByPrinciple; action's guidedByPrinciple text resolved to the case Obligation/Principle individual(s) by embedding shortlist + LLM multi-select (obligation-engagement grounding)
Transparency and Attribution; Professional Integrity
property=violatesObligation; action's violatesObligation text resolved to the case Obligation/Principle individual(s) by embedding shortlist + LLM multi-select (obligation-engagement grounding)
Credit and Attribution for Engineering Work; Avoid Deceptive Acts
Technological Innovation in Engineering Practice; Professional Competency
property=raisesObligation; action's raisesObligation text resolved to the case Obligation/Principle individual(s) by embedding shortlist + LLM multi-select (obligation-engagement grounding)
Direction and Control Over Work Product; Responsible Charge; Hold Paramount the Safety, Health, and Welfare of the Public
Client Confidentiality; Direction and Control Over Work Product
Respect for Client Privacy; Avoid Deceptive Acts
Professional Competency; Public Safety; Responsible Charge
Direction and Control Over Work Product; Responsible Charge; Hold Paramount the Safety, Health, and Welfare of the Public; Undertake Assignments Only When Qualified
Public Safety; Responsible Charge; Professional Competency
Hold Paramount the Safety, Health, and Welfare of the Public; Direction and Control Over Work Product; Responsible Charge; Undertake Assignments Only When Qualified
property=fulfillsObligation; action's fulfillsObligation text resolved to the case Obligation/Principle individual(s) by embedding shortlist + LLM multi-select (obligation-engagement grounding)
Direction and Control Over Work Product; Perform Services Within Competence
Professional Competency; Responsible Charge
Credit and Attribution for Engineering Work
property=invokedBy; component party text resolved to the case Agent(s) by embedding shortlist + LLM select
property=possessedBy; component party text resolved to the case Agent(s) by embedding shortlist + LLM select
property=obligatedParty; component party text resolved to the case Agent(s) by embedding shortlist + LLM select
property=constrainedEntity; component party text resolved to the case Agent(s) by embedding shortlist + LLM select
relation_type=has_client; property=hasClient
Engineer A, an environmental engineer with several years of experience and holding a professional engineering license, was retained by Client W
relation_type=peer; property=professionalPeerOf
Engineer A had relied on guidance and quality assurance reviews by their mentor and supervisor, Engineer B, to refine report drafts
relation_type=reviewed_by; property=workReviewedBy
Engineer A appears to be operating in a compromised manner -- namely, without the help of Engineer B
property=availableTo; resource used_by text resolved to the case Agent(s) by embedding shortlist + LLM multi-select
Engineer A used AI-assisted drafting tools to generate preliminary engineering design documents but did not disclose this to Client W when submitting the plans.
Engineer A used a new AI-assisted drafting tool without prior experience and conducted only a high-level review of the generated design documents
Engineer A did not proactively disclose to Client W that AI tools substantially contributed to the contaminant report and design documents
Engineer A's AI-generated contaminant report did not include citations of pertinent documents of technical authority
Engineer A used AI software to generate the initial draft of the contaminant report but did not disclose this to Client W
AI-generated design documents submitted by Engineer A omitted key safety features required by local regulations
Engineer A applied their professional seal to engineering design documents generated primarily by AI tools after only a cursory review
Engineer A did not disclose the use of AI tools to Client W in either the report or the design documents
Engineer A sealed and submitted AI-generated design documents after only a high-level review, without the substantive involvement in design and development required by responsible charge
Engineer A's insufficient review of AI-generated design documents resulted in omitted safety features and misaligned dimensions that could cause regulatory noncompliance and safety hazards
Engineer A used an AI drafting tool without prior experience or understanding of its accuracy limitations, producing design documents with technical errors that the engineer did not detect during review.
When Engineer B retired and became unavailable, Engineer A substituted AI software for the quality assurance review function that Engineer B had previously provided, without seeking alternative peer review or supervisory oversight.
When Engineer B became unavailable due to retirement, Engineer A proceeded with AI-assisted design work without seeking alternative review mechanisms
Engineer A conducted only a cursory review of AI-generated design documents despite having no prior experience with the AI drafting tool
Engineer A conducted a more thorough review of the AI-generated report, cross-checking key facts against professional journal articles and verifying phrasing
Engineer A accepted AI-generated design documents after only a high-level review without outlining solution guidelines, critically challenging outputs, or verifying that the AI-generated design met independent professional standards
Engineer A uploaded Client W's proprietary project information into a public-domain open-source AI interface without obtaining prior consent from Client W
Engineer A used open-source AI language processing software to generate the initial contaminant report draft and performed a thorough review, cross-checking key facts against professional sources and making textual adjustments
Engineer A made minor wording adjustments to AI-generated report text to personalize it and did not disclose AI authorship
When Engineer B retired and became unavailable, Engineer A substituted AI software for the quality assurance review function that Engineer B had previously provided
Engineer A conducted only a cursory review of AI-generated design documents despite having no prior experience with the AI drafting tool, resulting in submission of plans with misaligned dimensions and omitted safety features.
Engineer A used a new AI-assisted drafting tool without prior experience and conducted only a high-level review of the generated design documents, failing to detect misaligned dimensions and omitted safety features
Engineer A made minor wording adjustments to AI-generated report text to personalize it and did not disclose AI authorship, effectively presenting AI-generated content as independently authored professional writing.
Engineer A used AI software to generate the initial draft of the contaminant report but did not disclose this to Client W, submitting the report without citing the AI software or its large language models.
Engineer A conducted a more thorough review of the AI-generated report, cross-checking key facts against professional journal articles and verifying phrasing, demonstrating partial but not complete satisfaction of the verification obligatio
AI-generated design documents submitted by Engineer A omitted key safety features required by local regulations, creating a potential public safety risk
Engineer A's insufficient review of AI-generated design documents resulted in omitted safety features and misaligned dimensions that could cause regulatory noncompliance and safety hazards, violating the paramount obligation to protect publ
Engineer A was obligated to exercise genuine direction and control over the AI-assisted drafting tool by outlining solution guidelines and constraints, critically challenging outputs, and verifying results before accepting the AI-generated
When Engineer B retired and became unavailable, Engineer A was obligated to identify and implement an adequate alternative quality assurance process for their professional work products rather than substituting an unfamiliar AI tool
Engineer A was obligated to include citations to pertinent documents of technical authority in the AI-assisted contaminant report submitted to Client W.
Engineer A was obligated to obtain prior consent from Client W before uploading Client W's proprietary project information into a public-domain open-source AI interface.
Engineer A was obligated to conduct a thorough and competent review of the AI-generated engineering design documents, sufficient to identify regulatory non-compliance and technical errors, before submitting those documents to Client W.
Engineer A was obligated to accurately represent the AI-generated origin of the contaminant report content and to refrain from presenting AI-generated text as their own original intellectual work
Engineer A was obligated to conduct a comprehensive, experience-based verification of the AI-generated design documents with active involvement in the design and development process before sealing and submitting them.
Engineer A was obligated to exercise direction and control over the AI language processing tool used to generate the contaminant report draft
Engineer A was obligated to disclose to Client W that AI software was used to generate the initial draft of the contaminant report, including the use of large language models, before or at the time of submission.
Engineer A was obligated to conduct a thorough and competent review of the AI-generated contaminant report before submission
Engineer A was obligated to apply their professional seal to the engineering design documents only after conducting a review sufficient to take genuine professional responsibility for the content
Engineer A was obligated to verify that the AI-generated design documents included all safety features required by local regulations before submitting them to Client W.
Engineer A was obligated to proactively disclose to Client W that AI language processing software substantially contributed to the contaminant report.
Engineer A was obligated to ensure that the engineering design documents submitted to Client W included all safety features required by local regulations before submission
Engineer A was obligated to disclose to Client W that AI-assisted drafting tools were used to generate the preliminary engineering design documents before or at the time of submission.
property=affects; state affectedParties text resolved to the case Agent(s) by embedding shortlist + LLM multi-select
Engineer A; Client W; public safety; regulatory compliance
Engineer A; Client W; public affected by the design
Engineer A; Client W; public users of the groundwater infrastructure
property=activatedByEvent; description resolved to the endpoint by embedding shortlist + LLM select
AI software is used to generate a draft report, design document, or other professional work product
property=activatesConstraint; description resolved to the endpoint by embedding shortlist + LLM select
Should not omit disclosure of AI use where professional standards or client expectations require transparency
Should not seal or submit AI-generated work without thorough independent review
property=activatesObligation; description resolved to the endpoint by embedding shortlist + LLM select
Obligation to disclose AI tool use to clients and relevant parties
Obligation to verify accuracy, originality, and completeness of AI-generated content
Engineer A using AI software to generate the initial draft report and submitting it to Client W with a professional seal
AI tools are used to generate or substantially draft a professional work product
Should not represent AI-generated content as entirely self-authored without disclosure
Should not seal or certify work without disclosing the role of AI in its production where standards require
Obligation to correct any false impression created by non-disclosure
Obligation to disclose AI use to clients and relevant parties
property=terminatedByEvent; description resolved to the endpoint by embedding shortlist + LLM select
Disclosure of AI use is made to the client or relevant parties
Engineer A using AI language processing software to draft the groundwater monitoring report and submitting it to Client W
Confidential client data uploaded to an open or third-party platform
Must not represent the work product as fully confidential if data was exposed
Must not continue using the platform with client data absent consent
Duty to assess and mitigate downstream confidentiality risks
Should not represent outputs as professionally validated without adequate review
Should not rely on tool outputs without independent verification when tool behavior is not fully understood
Obligation to disclose use of unfamiliar tools to clients or supervisors where output reliability is uncertain
Obligation to conduct thorough verification of tool outputs before relying on them
AI tools substantially contributed to the work product
Engineer A entering information gathered from Client W into open-source AI software
Must not represent AI-generated content as solely the product of the professional's own analysis without disclosure
Must include citations and attribution consistent with professional standards
Duty to disclose substantial AI contributions to clients
Client W identifying misaligned dimensions and omission of required safety features in the AI-generated design documents
Obligation to assess whether deficiencies create safety or regulatory compliance risks
AI tool used to generate technical plans or documents
Must not affix signature or seal to AI-generated documents reviewed only at a high level
Duty to conduct experience-based quality assurance review
Duty to refrain from sealing or signing documents not under proper direction and control
Mentor or supervisor retires, resigns, or otherwise becomes unavailable
Should not proceed with work requiring mentorship support without establishing an alternative
Obligation to identify alternative quality assurance mechanisms
Obligation to seek additional support before undertaking work that previously required mentorship
Client W identifying that required safety features were omitted from the AI-generated design documents
Client W's discovery of omitted safety features and misaligned dimensions in the design documents
Reliance on AI or other tools substitutes for engineering judgment
Must not delegate engineering judgment entirely to AI tools
Duty to make engineering decisions personally or through supervised subordinates
Duty to refrain from sealing documents not under genuine direction and control
Engineer A's decision to use new open-source AI drafting software without prior experience
Engineer A electing to use a newly released AI-assisted drafting tool without prior experience or established verification protocols for its outputs
submission of both deliverables to Client W before Client W's review and identification of issues
When Client W reviewed the draft report ... Client W, however, noticed several issues with the AI-generated design documents ... Client W raised concerns about the accuracy and reliability of the engineering design and instructed Engineer A to revise the plans
submission of both deliverables to Client W
Client W's review and identification of issues
property=fromEntity; temporal relation's fromEntity text resolved to the case Action/Event individual by embedding shortlist + LLM select
Engineer A conducted a thorough review of the report, cross-checking key facts against professional journal articles and verifying the phrasing by running search engine queries
property=toEntity; temporal relation's toEntity text resolved to the case Action/Event individual by embedding shortlist + LLM select
Engineer A completed a cursory review of the AI-generated plans and adjusted certain elements to align with site-specific conditions.
Client W raised concerns about the accuracy and reliability of the engineering design and instructed Engineer A to revise the plans, ensuring that all elements satisfied the necessary professional and regulatory standards.
Engineer A completed a cursory review of the AI-generated plans and adjusted certain elements to align with site-specific conditions. Again, Engineer A did not cite the AI-assisted drafting tools they used to generate the engineering design documents.
Engineer A ... submitted the draft report to Client W for review, including language to clearly identify that the supplied report was a draft
Previously, Engineer A had relied on guidance and quality assurance reviews by their mentor and supervisor, Engineer B, to refine report drafts. But Engineer B recently retired and was no longer available to Engineer A in a work capacity.
Faced with the need to deliver both the report and the engineering design documents without the review by and mentorship from Engineer B, Engineer A opted to use open-sourced artificial intelligence (AI) software
Commissioned Engineer A to prepare a contaminant report and engineering design documents for a groundwater infrastructure site. Reviewed deliverables, identified quality inconsistencies in the report, and raised concerns about errors and regulatory omissions in the design documents.
project_type: Groundwater infrastructure modification
review_action: Identified misaligned dimensions and omitted safety features in design documents
Client W noticed several issues with the AI-generated design documents, including misaligned dimensions and an omission of key safety features required by local regulations
was retained by Client W to prepare a comprehensive report addressing the manufacture, use, and characteristics of an organic compound
Individual typed as existing ontology class 'Client Role'
Engineer A possessed the capability to accurately represent the AI-generated origin of the contaminant report and design documents but did not proactively disclose AI authorship to Client W, raising transparency concerns.
Engineer A used AI tools to generate both the contaminant report and design documents but did not disclose the AI contribution to Client W, who later identified inconsistencies and design errors.
Failure to proactively disclose to Client W that AI software substantially generated the contaminant report and design documents, which contributed to client confusion when inconsistencies were identified.
Given that Client W identified issues in the engineering design and questioned inconsistencies in the report, proactive disclosure could have prevented misunderstandings and strengthened trust.
While Engineer A reviewed the content, the lack of disclosure raises concerns about transparency.
Engineer A failed to exercise adequate AI direction and control capability over the AI-assisted drafting tool used for engineering design documents, conducting only a high-level review and not outlining solution guidelines or constraints before relying on AI-generated outputs.
Engineer A used a new AI-assisted drafting tool to generate preliminary engineering design documents and elected to conduct only a high-level review, without the comprehensive verification process required for responsible charge.
Failure to outline solution guidelines and constraints for the AI-assisted drafting tool, and failure to critically challenge AI-generated design outputs, resulting in undetected dimension errors and omitted safety features.
Engineer A elected to only conduct a high-level review and adjusted certain elements to align with site-specific conditions.
Much like directing an engineering intern to solve a problem, responsible use of AI requires an engineer to outline solution guidelines and constraints. Recommendations from the program or intern should not be blindly accepted, they should be considered and challenged and the resulting outputs should be understood.
Engineer A demonstrated partial exercise of AI direction and control capability for the contaminant report by performing a thorough review, cross-checking key facts, and making adjustments, but did not fully apply this capability to the AI-assisted design documents.
Engineer A used AI language processing software to draft a contaminant report and conducted a review sufficient to maintain direction and control over that document, but applied only a high-level review to the AI-generated design documents.
Thorough review and cross-checking of the AI-generated contaminant report against professional sources, with adjustments to text, satisfying direction and control requirements for that work product.
Because Engineer A performed a thorough review, cross-checked key facts against professional sources, and made adjustments to the text, the final document remained under Engineer A's direction and control, as required by Code section II.2.b
Engineer A lacked or failed to exercise the capability to recognize that use of AI software to generate the report draft and design documents triggered a professional disclosure obligation to Client W.
Engineer A used open-source AI software to generate an initial report draft and preliminary design documents for Client W but did not disclose this use in either submission.
Failure to cite AI software or large language model use in either the submitted report or the engineering design documents, despite using AI tools for both deliverables.
Again, Engineer A did not cite the AI-assisted drafting tools they used to generate the engineering design documents.
Engineer A did not cite their use of AI-software or its large language models, and submitted the draft report to Client W for review
Engineer A AI Output Verification Design
Engineer A failed to adequately apply AI output verification capability to the AI-generated engineering design documents, conducting only a high-level review that did not detect misaligned dimensions or omitted safety features.
Engineer A reviewed AI-generated design documents at a high level only, without the comprehensive verification required to take responsible charge, resulting in submission of documents with significant errors.
High-level review of AI-generated design documents that failed to identify misaligned dimensions and omitted regulatory safety features subsequently discovered by Client W.
By relying on AI-assisted tools without a comprehensive verification process of its output, Engineer A risked violating this requirement.
Engineer A elected to only conduct a high-level review and adjusted certain elements to align with site-specific conditions. When Client W reviewed the design documents, they found misaligned dimensions and key safety features (including those necessary for compliance with local regulations) were omitted.
Engineer A AI Output Verification Report
Engineer A applied AI output verification capability to the contaminant report by conducting a thorough review and cross-checking key facts against professional sources, meeting the standard for responsible charge over that work product.
Engineer A reviewed the AI-generated contaminant report sufficiently to maintain responsible charge, though the report still lacked required citations.
Thorough review of the AI-generated contaminant report, cross-checking against professional sources, and making textual adjustments before submission.
Because Engineer A performed a thorough review, cross-checked key facts against professional sources, and made adjustments to the text, the final document remained under Engineer A's direction and control
Engineer A possessed the capability to recognize that AI tools substantially contributed to the contaminant report and design documents and that this contribution warranted disclosure to Client W, but did not exercise this capability.
Engineer A used AI tools to generate both the contaminant report and engineering design documents for Client W without disclosing the AI contribution, contrary to emerging best practices and transparency obligations.
Failure to inform Client W that AI language processing software and AI-assisted drafting tools substantially contributed to the work products delivered, despite best practices indicating such disclosure is warranted.
BER Case 98-3 emphasized that engineers must acknowledge significant contributions by others. AI, while not a human contributor, fundamentally shaped the report and design documents, warranting disclosure under Code section III.9
There are currently no universal guidelines mandating AI disclosure in engineering work, but best practices suggest informing clients when AI substantially contributes to a work product.
Engineer A failed to accurately represent the AI-generated origin of the report content, presenting AI-synthesized text as professional work product without attribution, and made only minor wording adjustments to personalize AI-generated content.
Engineer A used AI software to generate the initial report draft, made minor adjustments to personalize the content, and submitted the report without disclosing the AI-generated origin of the polished introductory sections.
Submission of AI-generated report draft without authorship disclosure, combined with minor wording edits intended to personalize content, resulting in a report Client W observed read as if written by two different authors.
Client W noted that the introduction discussing the contaminant's manufacture, use, and characteristics to be exceptionally polished. The Client commented that the report read as if written by two different authors
Engineer A also made minor adjustments to some of the wording to personalize the content. Engineer A did not cite their use of AI-software or its large language models, and submitted the draft report to Client W for review
Engineer A input Client W's proprietary site data and groundwater monitoring information into open-source AI software to generate both the contaminant report draft and the preliminary design documents, without apparent consideration of the confidentiality implications of using an open-source platform.
Engineer A was constrained from uploading Client W's confidential site data and groundwater monitoring information into open-source AI software, as doing so exposed confidential client information to a third-party platform outside the protected professional relationship.
NSPE Code of Ethics confidentiality provisions, professional duty to protect client information
At the time of AI software use for both the report and design documents
Engineer A gathered the relevant information provided by Client W and relied on the AI software to synthesize the information
Engineer A input the information gathered from Client W into the AI software
The AI drafting software was also open-sourced.
Via class 'Confidential Data Platform Constraint': The Confidentiality Constraint class covers boundaries on information disclosure and privacy, which encompasses this scenario. However, the specific application to open-source AI platform data upload is a variant not explicitly addressed by the existing class. Matching as a variant of Confidentiality Constraint at medium confidence, while defining a more specific reusable subtype for AI platform contexts.
Engineer A had the professional background to recognize that uploading client proprietary information to a public-domain AI interface required prior client consent, but failed to exercise this capability before uploading Client W's data.
Engineer A used an open-source AI language processing tool to draft a contaminant report and uploaded Client W's confidential project information without obtaining prior consent.
Failure to obtain consent before uploading Client W's proprietary project information into an open-source AI interface, resulting in a confidentiality breach.
The facts here do not indicate Engineer A obtained permission from Client W to use the private information in the public domain.
When Engineer A uploaded Client W's information into the AI open-source interface, this was tantamount to placing the Client's private information in the public domain.
Via class 'Client Data Confidentiality Capability': The existing Confidentiality Scope Reasoning Capability addresses reasoning about limits of confidentiality obligations, but that class focuses on distinguishing between protecting review contents and procedurally improper conduct. This capability is more specifically about recognizing when tool use exposes client data to public domains and obtaining consent, which is a distinct application of confidentiality reasoning in the AI tool context. A medium-confidence match to the parent concept is appropriate, but the AI-tool-specific data upload dimension is sufficiently distinct to warrant a new class.
Confidentiality Scope Reasoning Capability
Engineer A used an AI-assisted drafting tool new to the market and conducted only a high-level review of the output, resulting in misaligned dimensions and omitted safety features. Engineer B's retirement had removed the quality assurance oversight Engineer A had previously relied upon.
Engineer A was required to actively direct and control the AI-assisted drafting process from conception through completion, including outlining solution guidelines and constraints before delegating to the AI tool, and critically evaluating all outputs rather than conducting only a high-level review after preparation.
NSPE Code of Ethics, Code section II.2.b; NSPE Position Statement No. 10-1778
Throughout the preparation and review of the AI-generated engineering design documents
NSPE defines 'Responsible Charge' in NSPE Position Statement No. 10-1778 as 'being actively engaged in the engineering process, from conception to completion.'
Reviewing drawings or documents after preparation without involvement in the design and development process does not satisfy the definition of Responsible Charge.
Engineering Design Error Recognition Capability
Engineer A failed to exercise adequate engineering design error recognition capability during review of the AI-generated design documents, missing misaligned dimensions and omitted safety features that were subsequently identified by Client W.
Engineer A reviewed AI-generated engineering design documents for a groundwater infrastructure modification project but conducted only a cursory review, missing regulatory deficiencies that Client W identified upon review.
Cursory review of AI-generated plans that failed to detect misaligned dimensions and the omission of key safety features required by local regulations.
Client W noticed several issues with the AI-generated design documents, including misaligned dimensions and an omission of key safety features required by local regulations. Client W raised concerns about the accuracy and reliability of the engineering design and instructed Engineer A to revise the plans, ensuring that all elements satisfied the necessary professional and regulatory standards.
Individual typed as existing ontology class 'Engineering Design Error Recognition Capability'
Engineer A Design Safety Regulatory Compliance
Client W identified that the AI-generated engineering design documents omitted key safety features required by local regulations, creating a public safety risk and requiring revision of the plans.
Engineer A was constrained to ensure that engineering design documents submitted to Client W included all safety features required by local regulations, and submission of documents omitting those features violated this inviolable safety boundary.
Local site safety regulations, NSPE Code of Ethics public safety provisions
At the time of preparing and submitting engineering design documents
Client W noticed several issues with the AI-generated design documents, including misaligned dimensions and an omission of key safety features required by local regulations.
Individual typed as existing ontology class 'Safety Constraint'
Engineer A Design Seal Responsible Charge
Engineer A relied on AI-assisted drafting tools to generate the preliminary design documents and performed only a cursory review before submission, raising the question of whether responsible charge was exercised as required for valid seal application.
Engineer A was constrained to apply their professional seal to the engineering design documents only if they had exercised genuine responsible charge over the preparation of those documents, which a cursory post-preparation review of AI-generated output did not satisfy.
State engineering seal law, NSPE responsible charge definition, NSPE Code of Ethics
At the time of sealing and submitting engineering design documents
submitted the draft report to Client W for review, including language to clearly identify that the supplied report was a draft, but applied their seal consistent with state law
Responsible Charge Verification Capability
Engineer A failed to exercise adequate responsible charge verification capability before sealing the AI-generated design documents, conducting only a cursory review insufficient to identify regulatory deficiencies.
Engineer A bore formal responsible charge over the AI-generated design documents and applied their seal, but the review conducted was insufficient to detect significant regulatory and dimensional errors.
Application of professional seal to design documents following only a cursory review, after which Client W identified misaligned dimensions and omitted safety features.
Engineer A was obligated to apply their professional seal to the engineering design documents only after conducting a review sufficient to take general responsibility for the work
Engineer A Domain Expertise Environmental
Engineer A possessed domain expertise as an experienced environmental engineer with strong technical expertise in groundwater monitoring data analysis and contaminant risk assessment, which was sufficient to maintain direction and control over the AI-generated contaminant report.
Engineer A was retained as an experienced environmental engineer with several years of experience and strong technical expertise, which distinguished the situation from BER Case 98-3 where the engineer lacked relevant competence entirely.
Application of environmental engineering expertise to review, cross-check, and adjust the AI-generated contaminant report, and recognition of site-specific conditions requiring adjustment in the design documents.
Engineer A, as an experienced environmental engineer, was competent to analyze groundwater monitoring data and assess contaminant risks.
The facts specifically note Engineer A has 'several years of experience' and 'strong technical expertise.' But the facts also note Engineer A appears to be operating in a compromised manner
Individual typed as existing ontology class 'Domain Expertise'
Engineer A Environmental Domain Competence
Engineer A possessed domain expertise in environmental engineering, including groundwater monitoring and contaminant analysis, developed over several years of licensed professional practice and site observation.
Engineer A was retained as a licensed environmental engineer to prepare a contaminant report and engineering design documents for groundwater infrastructure modifications, drawing on over a year of site observation.
Performance of groundwater monitoring data analysis over more than a year at the site, and preparation of a comprehensive contaminant report addressing manufacture, use, and characteristics of an emerging organic contaminant.
Engineer A, an environmental engineer with several years of experience and holding a professional engineering license, was retained by Client W to prepare a comprehensive report addressing the manufacture, use, and characteristics of an organic compound identified as an emerging contaminant of concern.
This work required Engineer A to perform an analysis of groundwater monitoring data from a site Engineer A had been observing for over a year.
Engineer A recognized the quality assurance gap created by Engineer B's retirement but selected an inadequate substitute mechanism, relying on unfamiliar AI tools without implementing alternative professional review.
When Engineer B retired and became unavailable, Engineer A faced delivery obligations for both a technical report and design documents without the quality assurance support that had previously compensated for Engineer A's lower confidence in technical writing.
Decision to use open-source AI software as a substitute for Engineer B's mentorship and quality assurance review, without prior experience with the tool and without arranging alternative professional oversight.
Engineer A is known for their strong technical expertise but is, personally, less confident in their technical writing. Previously, Engineer A had relied on guidance and quality assurance reviews by their mentor and supervisor, Engineer B, to refine report drafts. But Engineer B recently retired and was no longer available to Engineer A in a work capacity.
Faced with the need to deliver both the report and the engineering design documents without the review by and mentorship from Engineer B, Engineer A opted to use open-sourced artificial intelligence (AI) software to create an initial draft of the necessary report and to use AI-assisted drafting tools to generate preliminary design documents. The AI drafting software was new to the market and Engineer A had no previous experience with the tool.
Engineer A failed to exercise mentorship gap adaptation capability after Engineer B retired, continuing to rely on AI-generated outputs without implementing an adequate alternative quality assurance mechanism to replace Engineer B's oversight role.
Engineer B, who had served as mentor and quality assurance reviewer for Engineer A, retired and became unavailable. Engineer A did not replace this oversight function before proceeding with AI-assisted work products.
Failure to identify and implement an alternative quality assurance process after Engineer B became unavailable, resulting in reliance on AI-generated design documents without adequate independent review.
Engineer A, as the engineer in Responsible Charge of the project, is required to provide an experienced-based quality assurance review, engaging in critical discussions, mentorship, and professional development -- elements that AI cannot replicate.
the facts also note Engineer A appears to be operating in a compromised manner -- namely, without the help of Engineer B -- such that Engineer A relied on the AI-generated plans and specifications without proper oversight.
Engineer A exercised partial AI output verification capability for the report by cross-checking key facts and verifying phrasing, but did not extend equivalent verification to the engineering design documents.
Engineer A applied different levels of review to the two AI-generated deliverables, conducting a more thorough review of the report but only a cursory review of the design documents, which contained regulatory deficiencies identified by Client W.
Thorough fact-checking and search engine verification of report content versus cursory review of AI-generated design documents that missed misaligned dimensions and omitted safety features.
Not being familiar with the full functionality of the AI software, including the accuracy and originality of AI-generated text, Engineer A conducted a thorough review of the report, cross-checking key facts against professional journal articles
Engineer A Report Authorship Representation
Client W observed that the report read as if written by two different authors, with the AI-generated introduction appearing exceptionally polished compared to the sections Engineer A personally revised, suggesting the undisclosed AI authorship was perceptible and potentially misleading.
Engineer A was constrained by the non-deception principle from presenting AI-generated report content as their own original professional work product without disclosure, because doing so created a false impression about the authorship and intellectual origin of the submitted report.
NSPE Code of Ethics non-deception and honesty provisions
At the time of report submission and throughout the client relationship
The Client commented that the report read as if written by two different authors but was otherwise satisfactory.
Individual typed as existing ontology class 'Non-Deception'
The AI-generated contaminant report submitted to Client W did not include citations to pertinent documents of technical authority, which Engineer A failed to add during review.
Engineer A was required to include citations to pertinent documents of technical authority in the AI-assisted contaminant report, and the omission of such citations from the AI-generated content constituted a failure to meet the professional standard of care required by NSPE Code section III.9.
At the time of preparing and submitting the contaminant report to Client W
the facts do not indicate the AI-generated report included citations of pertinent documents of technical authority. Per Code section III.9, engineers are required to 'give credit for engineering work to those to whom credit is due,' so Engineer A's ethical use of the AI software would need to include appropriate citations.
Engineer A Responsible Charge Verification Design
Engineer A failed to apply responsible charge verification capability to the AI-generated design documents, substituting a high-level review for the comprehensive, experience-based verification required to support sealing and submission.
Engineer A bore formal responsible charge over the AI-assisted design documents but did not conduct a review sufficient to satisfy the NSPE definition of responsible charge, resulting in submission of documents with regulatory and safety deficiencies.
Sealing and submitting AI-generated design documents after only a high-level review, without the active involvement in design and development required by the definition of responsible charge.
Engineer A did not maintain responsible charge in violation of licensure law which violates Code section III.8.a.
NSPE defines 'Responsible Charge' in NSPE Position Statement No. 10-1778 as 'being actively engaged in the engineering process, from conception to completion. Engineering decisions must be personally made by the professional engineer or by others over which the professional engineer provides supervisory direction and control authority. Reviewing drawings or documents after preparation without involvement in the design and development process does not satisfy the definition of Responsible Charge.'
Engineer A, as an experienced environmental engineer, possessed the technical capability to identify required safety features in engineering design documents but failed to apply this capability during review of the AI-generated design documents.
Engineer A reviewed AI-generated engineering design documents at a high level only, failing to identify omitted safety features required for regulatory compliance before submitting the documents to Client W.
Failure to detect omitted safety features required by local regulations in the AI-generated design documents, which were subsequently identified by Client W.
When Client W reviewed the design documents, they found misaligned dimensions and key safety features (including those necessary for compliance with local regulations) were omitted.
failure to detect misaligned dimensions and omitted safety features further indicates that Engineer A did not exercise sufficient diligence. The errors in the AI-generated design documents could have led to regulatory noncompliance and safety hazards
Via class 'Safety Feature Recognition Capability': The existing Engineering Design Error Recognition Capability covers identifying errors, omissions, and deficiencies in engineering plans including safety implications. The present capability is a more specific instantiation focused on safety feature recognition and regulatory compliance checking. A medium-confidence match to the existing class is appropriate, but the regulatory compliance and safety feature identification dimension is sufficiently distinct to warrant a separate class for precision.
Engineer A, as an experienced environmental engineer, possessed the capability to identify and include citations to pertinent documents of technical authority in professional reports, but did not apply this capability when reviewing the AI-generated contaminant report.
The AI-generated contaminant report submitted to Client W lacked citations to pertinent technical authority documents, which Engineer A failed to supply during review.
Failure to ensure the AI-generated contaminant report included citations to pertinent documents of technical authority before submission to Client W.
Served as mentor and supervisor to Engineer A, providing guidance and quality assurance reviews of report drafts. Recently retired and became unavailable, removing the professional check that Engineer A had depended upon.
relationship_to_engineer_a: Mentor and supervisor
Engineer B recently retired and was no longer available to Engineer A in a work capacity
Via class 'Mentor Engineer': Mentor Engineer is a specialization of Professional Peer Role. The ontology lists Professional Peer Role as covering collegial relationships with mentoring and review obligations, which directly describes Engineer B's function. A distinct leaf class is warranted because the mentoring and supervisory quality-assurance dimension is more specific than generic peer review.
Engineer B's mentorship and QA reviews before Engineer B's retirement
Local regulatory requirements for groundwater infrastructure design safety features
an omission of key safety features required by local regulations
ensuring that all elements satisfied the necessary professional and regulatory standards
Client W identified that the AI-generated design documents omitted key safety features required by local regulations, establishing the regulatory standard against which Engineer A's design documents were found deficient.
Applicable local regulations at time of case
Individual typed as existing ontology class 'Legal Resource'
National Society of Professional Engineers
Engineer A did not cite their use of AI-software or its large language models
Engineer A opted to use open-sourced artificial intelligence (AI) software to create an initial draft of the necessary report
Governs Engineer A's professional obligations regarding competence, responsible use of AI tools, disclosure to clients, accuracy of sealed documents, and public safety in engineering deliverables.
Individual typed as existing ontology class 'Professional Code'
State legislature or professional licensing board
State Professional Engineering Licensing Statute governing seal application
applied their seal consistent with state law
Engineer A applied their professional seal to the draft report consistent with state law, making the conditions and requirements of that law directly relevant to the propriety of sealing AI-assisted work.
During drafting phase, before cursory review
During drafting phase, before thorough review
During design document preparation phase
During AI-assisted drafting phase, before submission
During both report and design document preparation phases
After receiving AI-generated design documents, before submission
Upon Client W's review of submitted design documents
At project initiation, after Engineer B's retirement
Upon Client W's review of submitted report
After Client W's review of design documents
Before project deliverables were due, over a year into site observation
After receiving the AI-generated report draft, before submission
AI generation of initial report draft before Engineer A's thorough review and cross-checking of the report
Engineer A's thorough review and cross-checking of the report
AI generation of preliminary design documents before Engineer A's cursory review of design documents
AI generation of preliminary design documents
Engineer A's cursory review of design documents
Client W's identification of issues in design documents before Client W's instruction to Engineer A to revise the plans
Client W's identification of issues in design documents
Client W's instruction to Engineer A to revise the plans
Engineer A's cursory review of design documents before submission of design documents to Client W
submission of design documents to Client W
Engineer A's thorough review of the report before submission of draft report to Client W
Engineer A's thorough review of the report
Engineer B's retirement before Engineer A's use of AI software for dual deliverables
Engineer A's use of AI software for dual deliverables
Design Defect Discovery → Revision Instruction Issued
Design Defect Discovery -- Client W finds defects in the submitted design
Revision Instruction Issued -- Client W formally instructs Engineer A to revise the plans
Had no defects been discovered, Client W would not have instructed Engineer A to revise the plans
Client W's discovery of misaligned dimensions and omitted safety features
Client W's review of the design documents
Discovery of defects in the design documents was enough to prompt a formal revision instruction
Client Data Upload to AI → AI Report Generation
Engineer A input the information gathered from Client W into the AI software, and, after a few refining prompts, received a first draft of the report generated by the AI software.
Client Data Upload to AI -- Engineer A inputs Client W information into the AI
AI Report Generation -- AI produces a first draft of the report
Without the data input and prompts, the AI would not have produced the draft report
Functioning AI language processing software
Input of Client W's gathered information
Inputting client data plus refining prompts was enough for the AI to generate a first draft report
AI Tool Adoption for Design → AI Design Generation
Engineer A entered the information gathered from Client W into the AI software and relied on the AI-assisted drafting tools to generate a preliminary design of the plans, including basic layouts and technical specifications.
AI Tool Adoption for Design -- Engineer A decides to use the AI-assisted drafting tool
AI Design Generation -- AI produces preliminary design documents with layouts and specifications
Without adopting and feeding the AI drafting tool, the preliminary AI-generated design would not exist
Input of Client W's gathered information into the AI
Inputting data and relying on AI-assisted drafting tools was enough to generate preliminary design plans
Draft Report Sealing and Submission → Report Stylistic Inconsistency
When Client W reviewed the draft report, Client W noted that the section analyzing the groundwater monitoring data would benefit from minor edits for grammar and clarity, but found the introduction discussing the contaminant's manufacture, use, and characteristics to be exceptionally polished.
Draft Report Sealing and Submission -- Engineer A seals and submits the draft report to Client W
Report Stylistic Inconsistency -- Client W notes the report reads as if written by two different authors
Without submission to Client W, the inconsistency would not have been noted by the client
Presence of stylistically divergent sections in the report
Submission of the draft report to Client W
Submitting the stylistically mixed report to the client was enough to enable the client's observation of inconsistency
AI Report Generation → Report Stylistic Inconsistency
AI Report Generation -- AI produces a highly polished introduction section
Thorough Report Review -- Engineer A reviews and makes only minor wording adjustments
Draft Report Sealing and Submission -- Report submitted with mixed authorial voices
Report Stylistic Inconsistency -- Client W observes the report reads as if written by two authors
Had the entire report been written in a uniform voice (or fully harmonized by Engineer A), the inconsistency would not have been observed
AI-generated polished introduction section
Engineer A's own writing style in the data analysis section
Juxtaposition of the two within one report
Combination of AI-generated polished prose alongside human-drafted content produced a noticeable stylistic mismatch
AI Tool Adoption for Report → AI Disclosure Omission for Report
AI Tool Adoption for Report -- Engineer A uses AI to draft the report
AI Disclosure Omission for Report -- Engineer A chooses not to cite the AI's contribution
Had Engineer A chosen to disclose, the omission and resulting transparency concern would not have arisen
Engineer A's decision not to cite the AI use
Substantial AI contribution to the report
Use of AI plus a deliberate choice not to cite it produced the disclosure omission
Dual Deliverable Pressure → AI Tool Adoption for Design
Faced with the need to deliver both the report and the engineering design documents without the review by and mentorship from Engineer B, Engineer A opted to use open-sourced artificial intelligence (AI) software to create an initial draft of the necessary report and to use AI-assisted drafting tools to generate preliminary design documents
Dual Deliverable Pressure -- Engineer A faces concurrent report and design deadlines
AI Tool Adoption for Design -- Engineer A decides to use AI-assisted drafting tools for design documents
Without the concurrent pressure and loss of support, Engineer A may not have turned to an unfamiliar AI drafting tool for design work
Availability of AI-assisted drafting tools
Concurrent deadlines for report and design
Combination of time pressure + lack of mentorship + availability of AI tools led to adoption for design
Supervisor Retirement → AI Tool Adoption for Report
Faced with the need to deliver both the report and the engineering design documents without the review by and mentorship from Engineer B, Engineer A opted to use open-sourced artificial intelligence (AI) software to create an initial draft of the necessary report
Supervisor Retirement -- Engineer B retires, removing prior QA review and mentorship
Dual Deliverable Pressure -- Engineer A faces concurrent report and design deadlines without support
AI Tool Adoption for Report -- Engineer A decides to use open-source AI software to draft the report
Had Engineer B remained available for review, Engineer A would likely have relied on mentorship rather than adopting unfamiliar AI software
Engineer A's lack of confidence in technical writing
Loss of Engineer B's quality assurance and mentorship
Combination of mentor unavailability + writing insecurity + delivery deadline drove adoption of AI software
Client Data Upload to AI → Client Data Exposure
AI Tool Adoption for Report -- Engineer A selects open-source AI software
Client Data Upload to AI -- Engineer A enters Client W's private information into the interface
Client Data Exposure -- Confidential information effectively placed in the public domain
Had Engineer A not uploaded the data, or used a secure/private tool with consent, exposure would not have occurred
Absence of client consent to public disclosure
Possession of Client W's confidential information
Use of an open-source (public) AI interface
Uploading confidential client data into an open-source AI interface was enough to expose it to the public domain
Cursory Design Document Review → Design Defect Discovery
AI Design Generation -- AI produces plans with misaligned dimensions and missing safety features
Cursory Design Document Review -- Engineer A reviews only superficially, missing the defects
Design Document Submission -- Defective documents submitted to Client W
Design Defect Discovery -- Client W identifies misaligned dimensions and omitted safety features
Had Engineer A conducted a comprehensive verification process, the misaligned dimensions and omitted safety features would likely have been caught and corrected before Client review
AI-generated plans containing misaligned dimensions and omitted safety features
Failure to detect the errors before submission
Only a cursory (insufficient) review by Engineer A
Combination of AI-generated defects + insufficient review allowed defects to remain in submitted documents
AI language processing software fundamentally shaped the contaminant report submitted to Client W, but Engineer A did not disclose this contribution or attribute it in the work product.
Engineer A was required to disclose and attribute the substantial contribution of AI language processing software to the contaminant report, consistent with the obligation to give credit for engineering work to those to whom credit is due under NSPE Code section III.9.
NSPE Code of Ethics, Code section III.9; BER Case 98-3
At the time of submitting the AI-assisted contaminant report to Client W
AI, while not a human contributor, fundamentally shaped the report and design documents, warranting disclosure under Code section III.9, '[e]ngineers shall give credit for engineering work to those to whom credit is due, and will recognize the proprietary interests of others.'
Engineer A uploaded Client W's confidential project information into a publicly accessible AI interface to generate a draft groundwater monitoring report, without obtaining Client W's consent to share that information externally.
Engineer A was prohibited from uploading Client W's confidential proprietary site data and groundwater monitoring information into an open-source AI interface without first obtaining Client W's prior consent, as doing so placed the client's private information in the public domain.
NSPE Code of Ethics, Code section II.1.c
At the time Engineer A used the open-source AI language processing tool to draft the contaminant report
Per Code section II.1.c, confidential information can only be shared with prior consent of the Client.
Engineer A had relied on Engineer B for quality assurance review of report drafts to compensate for self-assessed limitations in technical writing. After Engineer B retired, Engineer A turned to AI tools without implementing equivalent quality assurance, resulting in deficient work products.
Engineer A was constrained by the loss of Engineer B's mentorship and quality assurance review from delivering professional work products in areas of personal weakness, specifically technical writing, without implementing an adequate alternative quality assurance mechanism.
NSPE Code of Ethics competence provisions
Following Engineer B's retirement and throughout the Client W engagement
Engineer A is known for their strong technical expertise but is, personally, less confident in their technical writing.
Individual typed as existing ontology class 'Competence Constraint'
Engineer A Mentorship Loss Design Oversight
AI-generated engineering design documents
Technology Substitution Prohibition Principle
When Engineer B became unavailable due to retirement, Engineer A proceeded with AI-assisted design work without seeking alternative review mechanisms, resulting in inadequate oversight of work that contained safety-critical errors.
The loss of Engineer B's mentorship and quality assurance review created a gap in oversight that Engineer A was required to address by finding alternative review mechanisms rather than proceeding with a high-level self-review of novel AI-generated outputs.
The BER identified Engineer A as operating in a compromised manner without Engineer B's guidance, treating the failure to seek alternative oversight as a contributing factor in the ethical violations rather than a complete excuse.
The facts also note Engineer A appears to be operating in a compromised manner -- namely, without the help of Engineer B -- such that Engineer A relied on the AI-generated plans and specifications without proper oversight.
Engineer A used a new AI-assisted drafting tool without prior experience, conducted only a high-level review of the generated design documents, and did not outline solution guidelines or critically challenge the AI outputs, resulting in omitted safety features and misaligned dimensions.
Engineer A was obligated to exercise genuine direction and control over the AI-assisted drafting tool by outlining solution guidelines and constraints, critically challenging outputs, and verifying results before accepting the AI-generated design documents.
Throughout the AI-assisted design document preparation process
Engineer A relied on AI-generated plans and specifications without proper oversight following Engineer B's retirement, resulting in design documents with misaligned dimensions and omitted safety features required by local regulations.
Engineer A was prohibited from using the AI-assisted drafting tool as a substitute for independent engineering judgment, and was required to critically challenge and verify all AI-generated design outputs against professional standards before acceptance rather than relying on them without proper oversight.
NSPE Code of Ethics, Fundamental Canon I.1; BER Case 98-3
Engineer A relied on the AI-generated plans and specifications without proper oversight.
Recommendations from the program or intern should not be blindly accepted, they should be considered and challenged and the resulting outputs should be understood.
technology must not replace or be used as a substitute for engineering judgement.
Engineer A AI Tool Non-Disclosure Design
Engineering design documents submitted to Client W
Time pressure to deliver design documents
Disclosure of AI tool use in design documents is required so clients and regulators can evaluate the basis for the design and the degree of independent engineering judgment applied.
The principle was violated: Engineer A again chose not to disclose AI tool use in the design documents.
Engineer A Design Documents Competence Failure
Engineer A used a new AI-assisted drafting tool without prior experience and conducted only a high-level review of the generated design documents, failing to detect misaligned dimensions and omitted safety features, falling below the competence standard for AI-assisted design work.
Unlike the report, where Engineer A had domain expertise and conducted substantive verification, the design documents received insufficient scrutiny. The lack of experience with the tool combined with a high-level review rather than comprehensive verification constituted a failure of the AI verification competence obligation.
The BER found that Engineer A was not incompetent in the general sense but operated in a compromised manner without adequate oversight, resulting in an ethical violation through insufficient verification of AI-generated design outputs.
Engineer A also prepared draft design documents with a AI-assisted drafting tool that was new to the market. Engineer A elected to only conduct a high-level review and adjusted certain elements to align with site-specific conditions.
Engineer A Design Documents Verification
Engineer A performed only a cursory review of AI-generated design documents, which left misaligned dimensions and omitted safety features required by local regulations undetected until Client W identified them.
Engineer A was constrained to conduct a sufficiently thorough technical review of the AI-generated engineering design documents to identify regulatory omissions and dimensional errors before submission, and a cursory review did not satisfy this constraint.
NSPE Code of Ethics, state engineering seal law, local site safety regulations
Prior to submission of engineering design documents to Client W
Report and design document preparation without mentor oversight
The loss of Engineer B created a quality assurance gap that Engineer A was obligated to address through alternative review mechanisms. Substituting an unfamiliar AI tool without equivalent verification did not satisfy this obligation.
The principle was not satisfied: Engineer A chose AI substitution over alternative peer review, and the resulting work product contained errors that Engineer B's review might have caught.
Faced with the need to deliver both the report and the engineering design documents without the review by and mentorship from Engineer B, Engineer A opted to use open-sourced artificial intelligence (AI) software to create an initial draft of the necessary report and to use AI-assisted drafting tools to generate preliminary design documents.
Client W identified a quality concern in the report, signaling that the document's authorship was inconsistent even if factually adequate.
The submitted report exhibited a stylistic inconsistency noted by Client W, who observed that the document read as if written by two different authors, likely reflecting the blending of AI-generated and human-reviewed content.
Engineer A operated under elevated workload conditions with two simultaneous major deliverables and no supervisory backup.
Engineer A faced concurrent deadlines for both the comprehensive contaminant report and the groundwater infrastructure design documents, creating compounded workload pressure.
Engineer A AI Design Verification Failure
AI-generated engineering design documents for groundwater infrastructure
The principle was not satisfied: the cursory review was insufficient given Engineer A's unfamiliarity with the tool and the regulatory stakes of the design documents.
Client W, however, noticed several issues with the AI-generated design documents, including misaligned dimensions and an omission of key safety features required by local regulations.
The AI drafting software was new to the market and Engineer A had no previous experience with the tool.
Engineer A used open-source AI language processing software to generate the initial contaminant report draft and performed a thorough review, cross-checking key facts against professional sources and making textual adjustments, satisfying the competence requirement for AI-assisted report writing.
Competence in verification was satisfied for the report, but separate obligations regarding client data confidentiality and attribution were not fulfilled, meaning overall ethical compliance was incomplete.
Because Engineer A performed a thorough review, cross-checked key facts against professional sources, and made adjustments to the text, the final document remained under Engineer A's direction and control.
Engineer A, as an experienced environmental engineer, was competent to analyze groundwater monitoring data and assess contaminant risks. Because Engineer A performed a thorough review, cross-checked key facts against professional sources, and made adjustments to the text, the final document remained under Engineer A's direction and control.
From retention by Client W through completion of report and design documents
Transforms general competence and due care principles into specific verification and disclosure duties when deploying tools whose accuracy and limitations are not yet understood by the professional.
Engineer A's professional engagement with Client W
Completion and acceptance of all deliverables
Engineer A, an environmental engineer with several years of experience and holding a professional engineering license, was retained by Client W to prepare a comprehensive report
Client W retaining Engineer A to prepare the comprehensive report and engineering design documents
Individual typed as existing ontology class 'Client Relationship Established'
Engineer A lost access to mentor Engineer B's quality assurance reviews upon Engineer B's retirement and responded by using open-source AI software with which Engineer A had no prior experience, resulting in deficient design documents being submitted to Client W.
When Engineer B retired and became unavailable, Engineer A was obligated to identify and implement an adequate alternative quality assurance process for their professional work products rather than substituting an unfamiliar AI tool for the oversight function Engineer B had previously provided.
Upon Engineer B's retirement and throughout the project delivery period
Via class 'Mentorship Gap Competence Obligation': This obligation is a contextual specialization of the existing Competence Obligation, addressing the specific situation where a quality assurance mentor becomes unavailable. The existing class covers the general duty to practice within competence but does not address the specific duty to maintain quality assurance processes when supervisory support is lost. A new class is warranted to capture this distinct scenario type.
From Engineer B's retirement through the completion of the engagement
Transforms general competence obligations into specific duties to secure alternative quality assurance before undertaking work previously dependent on mentorship.
Engineer A's loss of access to Engineer B as mentor and quality assurance reviewer
Faced with the need to deliver both the report and the engineering design documents without the review by and mentorship from Engineer B
Engineer B's retirement from active work capacity
Engineer A used AI language processing software to generate the initial draft of a contaminant report but did not include citations to pertinent technical authority documents in the final report.
At the time of preparing and submitting the contaminant report
Per Code section III.9, engineers are required to 'give credit for engineering work to those to whom credit is due,' so Engineer A's ethical use of the AI software would need to include appropriate citations.
the facts do not indicate the AI-generated report included citations of pertinent documents of technical authority
Throughout the engagement, from initial assignment through submission of deliverables
Transforms the responsible charge and public safety principles into a concrete prohibition on passive post-hoc review as a substitute for active supervisory engagement throughout the engineering process.
Engineer A's self-assessed limitation in technical writing
Not resolved within the case; persists through the described events
Engineer A is known for their strong technical expertise but is, personally, less confident in their technical writing
Previously, Engineer A had relied on guidance and quality assurance reviews by their mentor and supervisor, Engineer B, to refine report drafts
Engineer A's recognition of personal weakness in technical writing and loss of mentorship support
Individual typed as existing ontology class 'Competence State'
Reduced quality assurance coverage due to absence of Engineer B
Fulfill professional engagement and deliver required work products to Client W
Undertake Assignments Only When Qualified
Engineer A accepted the dual engagement from Client W to produce a comprehensive contaminant report and engineering design documents for groundwater infrastructure modifications, without securing alternative quality assurance support after Engineer B's retirement.
Engineer A became obligated to revise the design documents, and the project moved into a remediation phase rather than proceeding to implementation.
Client W raised concerns about the defective design documents and formally instructed Engineer A to revise the plans to correct the identified deficiencies.
AI-assisted drafting tools generated the preliminary engineering design documents submitted to Client W, but Engineer A did not disclose this contribution.
Engineer A was required to disclose and attribute the substantial contribution of AI-assisted drafting tools to the engineering design documents submitted to Client W, consistent with the obligation to give credit for engineering work to those to whom credit is due.
At the time of submitting the AI-assisted engineering design documents to Client W
AI, while not a human contributor, fundamentally shaped the report and design documents, warranting disclosure under Code section III.9.
Engineer A accepted AI-generated design documents after only a high-level review without outlining solution guidelines, critically challenging outputs, or verifying that the AI-generated design met independent professional standards, effectively substituting AI output for engineering judgment.
The BER analogized responsible AI use to supervising an engineering intern: outputs must be outlined, challenged, and verified against independent professional standards before acceptance. Engineer A's high-level review fell short of this standard, constituting impermissible substitution.
The prohibition on substitution was treated as absolute in this context: no efficiency or novelty justification could excuse accepting AI-generated design outputs without substantive independent evaluation.
It is the [BER]'s position that technology has an important place in the practice of engineering, but it must never be a replacement of a substitute for engineering judgement.
Only after the engineer in Responsible Charge has satisfied themselves that the proposed solution is in accordance with their own and professional standards should the design/report be accepted. These are steps that, in this case, Engineer A chose not to follow.
Engineer A used open-source AI software to draft a groundwater contaminant report for Client W but did not disclose this use when submitting the draft, despite the AI having generated the substantive content of the introduction section.
Engineer A was constrained by professional ethics to disclose to Client W that AI language processing software was used to generate the initial draft of the contaminant report, and failure to do so constituted a violation of this constraint.
NSPE Code of Ethics, professional norms on honesty and transparency with clients
At the time of report submission to Client W
Client W proprietary site and contaminant data
Engineer A uploaded Client W's proprietary project information into a public-domain open-source AI interface without obtaining prior consent from Client W, constructively placing confidential client data in the public domain.
The confidentiality obligation under NSPE Code section II.1.c extends to incidental or constructive disclosure arising from tool selection. Uploading client data to a public AI interface is functionally equivalent to disclosure, regardless of intent.
The BER found no justification for the disclosure: Engineer A should have either obtained client consent or used a tool that did not expose client data to public access.
Via class 'Client Data Confidentiality Principle': The existing Confidentiality Principle covers the general obligation to protect client information. This instance adds a domain-specific qualifier about constructive disclosure through AI tool use, which is a meaningful variant warranting a more specific class rather than a direct match. The existing Confidentiality Principle does not address the incidental-disclosure-via-tool-selection scenario, so a variant class is appropriate.
From submission of the design documents to Client W through Client W's identification of the errors; correction not described in the facts
Transforms general professional responsibility and public safety principles into specific corrective action and notification duties once deficiencies in a submitted work product are confirmed.
Engineer A's submission of AI-generated design documents containing misaligned dimensions and omitted safety features required for regulatory compliance
Would require correction of all deficiencies and resubmission; not resolved within the case facts
The errors in the AI-generated design documents could have led to regulatory noncompliance and safety hazards.
Client W's review revealing misaligned dimensions and omitted safety features in the AI-generated design documents
From the decision to use AI software through submission of both deliverables
Engineer A's use of AI drafting software with no prior experience
Not being familiar with the full functionality of the AI software, including the accuracy and originality of AI-generated text
The AI drafting software was new to the market and Engineer A had no previous experience with the tool
Engineer A used an AI-assisted drafting tool that was new to the market and with which Engineer A had no established experience, resulting in a cursory review that failed to catch misaligned dimensions and omitted safety features in the submitted design documents.
Engineer A was constrained by competence boundaries from relying on an AI-assisted drafting tool with which they had no prior experience to generate engineering design documents for client submission without additional safeguards, because lack of familiarity with the tool's capabilities and limitations prevented adequate evaluation of its outputs.
NSPE Code of Ethics competence provisions, professional engineering standards
At the time of selecting and using the AI drafting tool for Client W's design documents
Via class 'Unfamiliar Tool Competence Constraint': The existing Competence Constraint class covers boundaries defined by agent capabilities and technical limitations, which encompasses this scenario. The unfamiliar tool variant is a specific application of that general class. Matching as a variant at medium confidence while defining a more specific reusable subtype for unfamiliar tool deployment contexts.
Client W unaware of AI involvement and therefore unable to apply appropriate scrutiny or make informed decisions about the reliability of the designs
Submit the design documents as a professional work product without separately identifying AI as a contributing tool
Awareness of professional attribution norms for AI-assisted design work
Judgment about transparency obligations to clients
Engineer A chose not to disclose the use of AI-assisted drafting tools when submitting the engineering design documents to Client W, providing no indication that the plans and specifications were substantially generated by AI software.
Engineer A used an open-source AI language processing tool to generate a contaminant report draft, uploading Client W's confidential project data into the public-domain interface without documented client consent.
Prior to uploading any client data into the AI tool
Via class 'Client Data Confidentiality Obligation': This is a specific variant of the existing Confidentiality Obligation, narrowed to the context of uploading client data into public-domain AI tools without consent. The existing class covers duty to protect confidential information generally; this specialization addresses the AI-tool transmission vector.
From Client W's identification of deficiencies through correction and resubmission
The engineering design documents found to contain errors and regulatory omissions
Engineer A revising the plans to meet professional and regulatory standards
Client W raised concerns about the accuracy and reliability of the engineering design and instructed Engineer A to revise the plans, ensuring that all elements satisfied the necessary professional and regulatory standards
From Engineer A's use of the AI drafting tool through Client W's discovery of errors in the design documents
Transforms the responsible charge and public safety principles into a concrete obligation to apply at least the same level of scrutiny to AI-generated work as to human-generated work, including critical engagement with outputs rather than passive acceptance.
Engineer A's high-level review of AI-assisted drafting tool output for the design documents, which left misaligned dimensions and omitted safety features undetected
Client W's identification of errors in the design documents; full remediation not described in the facts
Engineer A electing to conduct only a high-level review of AI-generated design documents rather than a comprehensive verification
Engineer A conducted a more thorough review of the AI-generated report, cross-checking key facts against professional journal articles and verifying phrasing, demonstrating partial but not complete satisfaction of the verification obligation.
The principle was partially satisfied for the report but the stylistic inconsistency identified by the client indicates residual gaps in verification.
Engineer A conducted a thorough review of the report, cross-checking key facts against professional journal articles and verifying the phrasing by running search engine queries to ensure the content did not match any existing language.
From submission of the report through the case's described events
Transforms honesty and transparency principles into specific disclosure obligations when AI tools have played a material role in producing a professional work product.
Engineer A's omission of disclosure of AI use in the report
Engineer A submitting the AI-assisted report to Client W without disclosing AI use
Client W's confidential site data entered a third-party AI system, creating a potential confidentiality breach outside Engineer A's control.
When Engineer A uploaded client monitoring data and site information to the open-source AI software, that proprietary client data was transmitted to and processed by a third-party system outside Engineer A's control.
Contaminant report submitted to Client W
Client perception of professional capability
Personalizing AI-generated text without disclosing its origin misrepresents the intellectual authorship of the work product. The client's observation that the report read as if written by two authors indicates the concealment was imperfect and material.
Intellectual Honesty in Authorship Principle
The principle was violated: Engineer A's minor edits did not transform AI-generated content into independently authored work, and the absence of disclosure misrepresented the work's intellectual origin.
Engineer A also made minor adjustments to some of the wording to personalize the content.
Via class 'Intellectual Honesty in Authorship Principle': Intellectual Honesty exists in the ontology as a general principle. The AI authorship context is a specific and emergent variant that the existing class does not fully address, particularly the novel question of whether presenting AI-generated text without disclosure constitutes a failure of intellectual honesty. The match is a variant rather than a direct match, warranting a new class that specializes the existing principle for AI authorship contexts.
Engineer A also made minor adjustments to some of the wording to personalize the content. Engineer A did not cite their use of AI-software or its large language models, and submitted the draft report to Client W for review.
Engineer A used an AI-assisted drafting tool new to the market to generate preliminary engineering design documents for Client W but did not disclose this use, and the documents were later found to contain misaligned dimensions and omitted safety features.
Engineer A was constrained by professional ethics to disclose to Client W that AI-assisted drafting tools were used to generate the preliminary engineering design documents, and failure to do so constituted a violation of this constraint.
At the time of design document submission to Client W
AI-generated contaminant report and design documents
Engineer A did not proactively disclose to Client W that AI tools substantially contributed to the contaminant report and design documents, and Client W subsequently identified inconsistencies and errors that proactive disclosure could have helped prevent or address.
While no universal guidelines mandate AI disclosure, best practices and the transparency obligations underlying NSPE Code section III.9 support informing clients when AI substantially shapes a work product, particularly when the client may need to assess reliability or provenance.
The BER found that proactive disclosure would have strengthened trust and prevented misunderstandings, treating it as a best-practice obligation even in the absence of a universal mandate.
While Engineer A reviewed the content, the lack of disclosure raises concerns about transparency. BER Case 98-3 emphasized that engineers must acknowledge significant contributions by others.
Engineer A's AI-generated contaminant report did not include citations of pertinent documents of technical authority, and Engineer A did not disclose to Client W that AI substantially contributed to the report, failing the attribution and credit obligation under NSPE Code section III.9.
Attribution obligations require both citation of technical sources synthesized by AI and disclosure of AI's substantial role in shaping the work product. Absence of citations undermines the professional record; absence of disclosure prevents the client from assessing provenance.
The BER found that proactive disclosure of AI contributions and proper citation of technical authority were required for ethical use of AI in report preparation, and their absence rendered Engineer A's use of AI less than ethical.
The facts do not indicate the AI-generated report included citations of pertinent documents of technical authority.
Engineer A AI Tool Non-Disclosure Report
Client expectations for polished deliverables
Professional norms of transparency require disclosure of AI tool use so clients can assess the provenance and reliability of the work product. Non-disclosure here concealed a material fact about how the report was produced.
The principle was violated: Engineer A chose not to disclose AI use, prioritizing seamless delivery over transparent representation of authorship.
Licensed environmental engineer retained by Client W to prepare a comprehensive contaminant report and engineering design documents for groundwater infrastructure modifications at a monitored site.
Environmental engineering, groundwater monitoring
Engineer A was tasked to develop engineering design documents (plans and specifications) for modifications to groundwater infrastructure at the same site
Via class 'Environmental Engineer': Environmental Engineer is a specialization of Engineer Role in the ontology. The ontology has Engineer Role as the occupational archetype head for all engineer specializations, so this is a medium-confidence match to the parent. A distinct leaf class is warranted because environmental engineering carries domain-specific obligations around contaminant reporting and groundwater infrastructure not captured by the generic Engineer Role.
Engineer A Regulatory Non-Compliance Design
From submission of the deficient design documents through correction and resubmission
The engineering design documents that omit safety features required by local regulations
Engineer A revising the plans to satisfy regulatory requirements
instructed Engineer A to revise the plans, ensuring that all elements satisfied the necessary professional and regulatory standards
Client W identifying that the AI-generated design documents omit key safety features required by local regulations
Individual typed as existing ontology class 'Non-Compliant State'
Report and design documents submitted to Client W
Engineer A did not disclose the use of AI tools to Client W in either the report or the design documents, depriving the client of information material to evaluating the work product's reliability and the basis for the engineer's professional judgments.
Transparency in professional relationships requires engineers to be open about the methods and tools used to produce work products, particularly when those tools are novel and their reliability is uncertain.
The principle was not satisfied: non-disclosure of AI tool use prevented the client from making an informed assessment of the work product's provenance.
Individual typed as existing ontology class 'Transparency'
Engineer A did not cite their use of AI-software or its large language models, and submitted the draft report to Client W for review, including language to clearly identify that the supplied report was a draft, but applied their seal consistent with state law.
From Engineer A's adoption of the new AI drafting tool through the production and submission of the design documents
Engineer A's use of an AI-assisted drafting tool that was new to the market and with which Engineer A had no established experience
Would require Engineer A to develop sufficient familiarity and verification protocols for the tool before relying on its outputs for professional work products
Engineer A also prepared draft design documents with an AI-assisted drafting tool that was new to the market.
Engineer A lost access to supervisory oversight and quality assurance support, leaving the project without a senior review resource.
Engineer B, Engineer A's mentor and supervisor, retired before project deliverables were due, removing the primary quality assurance resource from the project.
Engineer A made minor wording adjustments to AI-generated report text to personalize it and submitted the report without disclosing AI authorship, effectively presenting the AI-generated content as their own original professional writing.
Engineer A was obligated to accurately represent the AI-generated origin of the contaminant report content and to refrain from presenting AI-generated text as their own original intellectual work by making only minor wording adjustments without attribution.
At the time of preparing and submitting the draft contaminant report
From report submission through case analysis; no disclosure is noted in the facts
Engineer A's use of AI language processing software to draft the groundwater monitoring report without disclosing that use to Client W or including AI attribution
Would require proactive disclosure to Client W and inclusion of appropriate citations
The facts also do not indicate the AI-generated report included citations of pertinent documents of technical authority.
Engineer A submitting the AI-assisted report to Client W without disclosure or attribution
Engineer A Confidential Client Data AI Input
From Engineer A inputting Client W's information into the AI software through the engagement
Transforms the general honesty and attribution principles into a concrete obligation to proactively disclose when AI has substantially shaped a work product, even absent a universal regulatory mandate to do so.
Client W's proprietary site data and groundwater monitoring information input into open-source AI software
The AI drafting software was also open-sourced
Individual typed as existing ontology class 'Confidential Information Held'
Engineer A conducted only a cursory review of AI-generated design documents produced by a tool with which Engineer A had no prior experience, resulting in submission of documents with misaligned dimensions and omitted safety features required by local regulations.
Via class 'AI Output Verification Obligation': This obligation is a specialization of the existing Competence Obligation, applied specifically to the context of AI-generated outputs. The existing class covers the general duty to practice within competence, but does not address the specific verification duty that arises when AI tools are used to generate professional work products. A new class is warranted to capture this distinct obligation type.
Engineer A Public Safety Design Omission
From identification of the safety feature omissions through correction of the design documents
public users of the groundwater infrastructure
The risk to public safety created by the omission of required safety features in the engineering design documents
Engineer A revising the plans to include all required safety features
Client W raised concerns about the accuracy and reliability of the engineering design
Individual typed as existing ontology class 'Public Safety at Risk'
From Engineer A's use of the AI drafting tool through the submission of deficient design documents; persists until responsible charge obligations are properly satisfied
Engineer A's failure to maintain active supervisory direction and control over AI-generated design documents, relying on post-preparation review rather than engagement throughout the design process
Would require Engineer A to re-engage with comprehensive verification and active supervisory direction; not resolved within the case facts
Engineer A, as the engineer in Responsible Charge of the project, is required to provide an experienced-based quality assurance review, engaging in critical discussions, mentorship, and professional development.
Engineer A sealing and submitting AI-generated design documents reviewed only at a high level without active involvement in the design and development process
From submission of the design documents through the case's described events
Engineer A's omission of disclosure of AI use in the engineering design documents
Engineer A did not cite the AI-assisted drafting tools they used to generate the engineering design documents
Engineer A submitting the AI-assisted design documents to Client W without disclosing AI use
Risk of errors in AI-generated designs given Engineer A's unfamiliarity with the tool and absence of comprehensive review process
Technological Innovation in Engineering Practice
AI-Assisted Design Document Drafting State
Efficiently generate preliminary design documents to meet project deliverable requirements without the benefit of Engineer B's oversight
Hold Paramount the Safety, Health, and Welfare of the Public
Engineering design expertise for groundwater infrastructure
Proficiency with AI drafting tool capabilities and limitations
Regulatory knowledge for local safety requirements
Engineer A decided to use AI-assisted drafting tools, which were new to the market and with which Engineer A had no prior experience, to generate preliminary engineering design documents including basic layouts and technical specifications for the groundwater infrastructure modifications.
Potential exposure of confidential client information to the public domain
Provide the AI software with sufficient project context to generate relevant and accurate draft content
Understanding of data privacy implications of open-source AI platforms
Engineer A entered Client W's private project information into the open-source AI software interface to generate report content and design documents, effectively placing confidential client data into a public-domain platform without obtaining Client W's prior consent.
Sealing a document with undisclosed AI contributions and without full citation of technical authority sources
Fulfill the contractual deliverable and assert professional responsibility over the report content in accordance with state licensure requirements
Environmental engineering expertise sufficient to take responsible charge of the report
Understanding of sealing obligations under state law
Engineer A applied their professional seal to the draft report consistent with state law and submitted it to Client W with language clearly identifying it as a draft.
From AI generation of the design documents through Client W's identification of deficiencies
Transforms general honesty, competence, and professional accountability principles into specific duties of disclosure, verification, and independent review when AI tools contribute substantially to a professional work product.
The engineering design documents generated using AI-assisted drafting tools and submitted to Client W
Client W's identification of deficiencies and instruction to revise
Engineer A completed a cursory review of the AI-generated plans and adjusted certain elements to align with site-specific conditions
Engineer A entered the information gathered from Client W into the AI software and relied on the AI-assisted drafting tools to generate a preliminary design of the plans
Engineer A using AI-assisted drafting tools to generate preliminary design plans and specifications and submitting them to Client W
Engineer A sealed and submitted AI-generated design documents after only a high-level review, without substantive involvement in the design and development process, resulting in omitted safety features and misaligned dimensions.
Responsible Charge Verification Obligation
During preparation and prior to sealing and submission of the design documents
Engineer A's oversight of engineering plans was inadequate, raising ethical concerns. AI-generated technical work requires at least the same level of scrutiny as human-created work.
Via class 'Responsible Charge Verification Obligation': This obligation is closely related to the already-extracted Responsible Charge Seal Obligation but emphasizes the verification and active-involvement dimension rather than the sealing act itself. It is a variant of that class focusing on the process of responsible charge rather than the sealing act.
Engineer A used open-source AI software to generate the initial contaminant report draft, then performed a thorough review, cross-checked key facts against professional journal articles, and made adjustments to the text.
Engineer A was obligated to exercise direction and control over the AI language processing tool used to generate the contaminant report draft, which was satisfied by thorough review and cross-checking against professional sources.
During preparation and review of the contaminant report
Because Engineer A performed a thorough review, cross-checked key facts against professional sources, and made adjustments to the text, the final document remained under Engineer A's direction and control, as required by Code section II.2.b.
Groundwater infrastructure design documents
AI-generated design documents submitted by Engineer A omitted key safety features required by local regulations, creating a potential public safety risk that the client identified during review.
The omission of required safety features from engineering design documents implicates the fundamental obligation to hold public safety paramount. The engineer's cursory review of AI-generated plans failed to protect against this risk.
The principle was not adequately served: the failure to verify AI-generated safety-critical design elements allowed a public safety risk to reach the client review stage rather than being caught by the engineer.
Individual typed as existing ontology class 'Public Safety Paramount'
Sealed engineering design documents for groundwater infrastructure
Engineer A applied their professional seal to engineering design documents generated primarily by AI tools after only a cursory review, submitting plans that contained misaligned dimensions and omitted required safety features.
Applying a professional seal represents a public declaration of responsible charge and independent professional judgment. A cursory review of AI-generated documents that fails to catch regulatory omissions does not satisfy the substantive oversight the seal implies.
The principle was violated: the seal was applied without the substantive independent review it represents, as evidenced by the errors identified by the client.
submitted the draft report to Client W for review, including language to clearly identify that the supplied report was a draft, but applied their seal consistent with state law.
Failure to give credit to AI contributions as required by attribution norms
Potential client misunderstanding about authorship
Submit the report as a professional work product without drawing attention to AI involvement, consistent with then-current absence of universal mandatory disclosure guidelines
Awareness of professional attribution norms
Judgment about disclosure obligations for AI-assisted work
Engineer A chose not to disclose the use of AI language processing software or cite its contributions when submitting the draft report to Client W, and did not include citations to the AI-generated content or the large language models used.
Engineer A used open-source AI software to generate the initial draft of a contaminant report for Client W and submitted the report without disclosing AI involvement, making only minor wording adjustments to personalize the content.
At the time of submitting the draft report to Client W
From the moment Engineer A uploaded Client W's data to the AI platform through the point of case analysis; no remediation or consent is noted in the facts
Transforms the general confidentiality principle into a concrete obligation to notify the client and obtain consent before using open-domain AI tools with private client data.
Engineer A's upload of Client W's confidential information to an open-source AI interface
Not terminated within the case facts; would require client notification, consent, or confirmed data removal
Engineer A uploading Client W's private information into an open-source AI interface without prior consent
Bore formal responsible charge over the AI-assisted design documents and contaminant report, but conducted only a high-level review of AI-generated engineering plans, failing to satisfy the active engagement and supervisory control requirements of responsible charge, resulting in undetected dimensional errors and omitted safety features.
responsible_charge_status: Deficient under NSPE Position Statement No. 10-1778
By relying on AI-assisted tools without a comprehensive verification process of its output, Engineer A risked violating this requirement
Engineer A did not maintain responsible charge in violation of licensure law
Engineer A, as the engineer in Responsible Charge of the project, is required to provide an experienced-based quality assurance review, engaging in critical discussions, mentorship, and professional development
Via class 'Responsible Charge Engineer': Responsible Charge Engineer is a specialization of Engineer Role, distinguished by the formal licensure-law concept of responsible charge, which imposes active engagement and supervisory control obligations beyond general engineering practice. No existing extracted class captures this specific licensure-based accountability framing.
Engineer A Responsible Charge Design Failure
Engineer A sealed and submitted AI-generated design documents after only a high-level review, without the substantive involvement in design and development required by responsible charge, resulting in documents with safety-critical errors reaching the client.
The BER found no acceptable balance: responsible charge cannot be satisfied by reviewing AI outputs without substantive involvement in the design process, regardless of the efficiency benefits of AI-assisted drafting.
Engineer A Design AI Output Verification
Engineer A conducted only a high-level review of AI-generated design documents, failing to identify misaligned dimensions and omitted safety features required by local regulations, resulting in a deficient and noncompliant submission.
Engineer A was required to conduct a sufficiently thorough independent technical review of the AI-generated engineering design documents before submission and sealing, sufficient to identify misaligned dimensions, omitted safety features, and regulatory noncompliance, and to take genuine responsible charge of the final work product.
NSPE Code of Ethics, Fundamental Canon I.1; Code section II.2.b; NSPE Position Statement No. 10-1778
Before submission and sealing of the AI-generated engineering design documents
AI-generated technical work requires at least the same level of scrutiny as human-created work.
failure to detect misaligned dimensions and omitted safety features further indicates that Engineer A did not exercise sufficient diligence.
From submission of the deficient design documents through Client W's identification of errors; full remediation not described
The risk to public safety arising from omitted safety features and misaligned dimensions in the AI-generated design documents submitted by Engineer A
Would require correction of all safety-critical omissions and regulatory compliance verification
The errors in the AI-generated design documents could have led to regulatory noncompliance and safety hazards, conflicting with the Fundamental Canon I.1, 'hold paramount the safety, health, and welfare of the public'.
key safety features (including those necessary for compliance with local regulations) were omitted.
Engineer A conducted a thorough review of the AI-generated report, cross-checking key facts against professional journal articles and running search engine queries to verify originality of phrasing, before submitting the draft report to Client W.
Engineer A was obligated to conduct a thorough and competent review of the AI-generated contaminant report before submission, which Engineer A substantially met by cross-checking key facts against professional journal articles and verifying phrasing against existing published language.
Prior to submission of the draft contaminant report to Client W
Review may not catch all deficiencies given unfamiliarity with AI output characteristics
Report Under Engineer Direction and Control State
Ensure factual accuracy, originality, and professional quality of the report before submission to Client W
Critical evaluation of AI-generated text
Environmental engineering domain expertise
Technical knowledge to fact-check environmental contaminant content
Engineer A conducted a thorough review of the AI-generated report draft, cross-checking key facts against professional journal articles, verifying phrasing through search engine queries to check for copied language, and making minor wording adjustments to personalize the content.
From AI generation of the report draft through submission to Client W
The draft report generated using AI software and submitted to Client W
Client W's review and identification of inconsistencies; pending revision
Engineer A relied on the AI software to synthesize the information and generate an initial draft of the necessary report
Engineer A applied their professional seal to AI-generated engineering design documents after only a cursory review, and the sealed documents contained misaligned dimensions and omitted safety features required by local regulations.
Engineer A was obligated to apply their professional seal to the engineering design documents only after conducting a review sufficient to take genuine professional responsibility for the content, including verification that all regulatory safety requirements were satisfied.
At the time of sealing and submitting the engineering design documents
Engineer A applied their seal consistent with state law.
AI-generated engineering design documents with omitted safety features
Engineer A's insufficient review of AI-generated design documents resulted in omitted safety features and misaligned dimensions that could cause regulatory noncompliance and safety hazards, violating the paramount obligation to protect public safety.
The omission of safety features required for regulatory compliance directly implicated public welfare. The BER treated this as the most serious ethical concern arising from Engineer A's use of AI-assisted drafting, grounding the responsible charge and competence violations in the fundamental canon.
Public safety paramountcy was not balanced against competing considerations; it served as the foundational reason why insufficient AI review is ethically impermissible regardless of efficiency or tool novelty.
While AI-assisted drafting can be beneficial, the identified errors suggest insufficient review, which could compromise public welfare and impinge on Engineer A's ethical and professional obligations.
Engineer A submitted AI-generated design documents that omitted key safety features required by local regulations, creating potential public safety hazards and regulatory noncompliance.
Prior to submission of the design documents to Client W
Client W reviewed the design documents, they found misaligned dimensions and key safety features (including those necessary for compliance with local regulations) were omitted.
Via class 'Safety Feature Compliance Obligation': This obligation is a variant of the already-extracted Regulatory Safety Compliance Obligation, with specific emphasis on verifying that safety features required by local regulations are not omitted from AI-generated design documents.
Potential confidentiality exposure from uploading client data to open-source platform
Uncertainty about AI output accuracy and originality
Compensate for reduced writing confidence and lack of mentorship by using AI to produce a workable first draft efficiently
Familiarity with AI tool capabilities and limitations
Judgment to evaluate AI-generated technical content
Engineer A decided to use new, open-source AI language processing software to generate an initial draft of the contaminant report rather than drafting it independently or seeking an alternative expert reviewer.
Engineer A Professional Competence AI Use
AI-assisted engineering design documents
Professional competence requires engineers to understand the tools they use in professional practice. Deploying an unfamiliar AI tool on safety-critical design work without adequate understanding of its limitations falls below the competence standard.
The principle was not satisfied: Engineer A's lack of familiarity with the AI tool contributed to a failure to detect errors in the design documents.
Individual typed as existing ontology class 'Professional Competence'
Used open-source AI software to generate an initial report draft and preliminary engineering design documents without prior experience with the tool, applied a professional seal to the output, and did not disclose AI use to the client. The AI-generated design documents contained misaligned dimensions and omitted key safety features required by local regulations.
ai_tool_experience: None prior to this engagement
Engineer A did not cite their use of AI-software or its large language models, and submitted the draft report to Client W for review, including language to clearly identify that the supplied report was a draft, but applied their seal consistent with state law
Engineer A opted to use open-sourced artificial intelligence (AI) software to create an initial draft of the necessary report and to use AI-assisted drafting tools to generate preliminary design documents
From the report's submission to Client W through the case analysis period
Engineer A's groundwater monitoring report substantially drafted by AI language processing software
Would require disclosure, attribution, and confirmation that the work product meets professional standards; partially addressed by Engineer A's cross-checking but attribution and consent issues remain
AI, while not a human contributor, fundamentally shaped the report and design documents.
Engineer A performed a thorough review, cross-checked key facts against professional sources, and made adjustments to the text.
Engineer A used AI language processing software to draft a groundwater monitoring report.
Engineer A used open-source AI software to generate the initial draft of the contaminant report but did not disclose this to Client W when submitting the final report.
At the time of delivering the contaminant report to Client W
BER Case 98-3 emphasized that engineers must acknowledge significant contributions by others. AI, while not a human contributor, fundamentally shaped the report and design documents, warranting disclosure under Code section III.9.
Via class 'AI Contribution Disclosure Obligation': This obligation is substantively the same concept as the already-extracted AI Tool Disclosure Obligation. The present class emphasizes the proactive and best-practice dimension but covers the same duty to disclose AI contribution to clients and stakeholders.
The design documents were identified as non-compliant and technically deficient, creating a public safety risk and triggering a revision requirement.
Client W's review of the submitted design documents revealed misaligned dimensions and omitted safety features required by local regulations, constituting a failure of the design deliverable to meet regulatory and professional standards.
Risk that undetected errors could cause regulatory noncompliance or safety hazards if the documents were acted upon
Deficient Design Documents Submitted State
Fulfill the contractual deliverable for engineering design documents and advance the project for Client W's review
Comprehensive design review and verification
Responsible charge over engineering plans and specifications
Dual Deliverable Obligation for Design Documents
Engineer A submitted the AI-generated engineering design documents to Client W without comprehensive review, without disclosure of AI use, and without verifying that all elements satisfied applicable professional and regulatory standards, including local safety feature requirements.
AI-generated design documents submitted by Engineer A omitted key safety features required by local regulations, which Client W identified upon review and required Engineer A to correct.
Engineer A was obligated to ensure that the engineering design documents submitted to Client W included all safety features required by local regulations before submission, regardless of whether those documents were generated by AI tools.
Via class 'Regulatory Safety Compliance Obligation': This obligation is a specialization of the existing Safety Obligation, focused specifically on regulatory compliance in design documents. The existing Safety Obligation covers the general duty to hold paramount public safety, and the external reference standard 'Comply with Applicable Safety Standards' is closely related. A distinct class is warranted because this obligation specifically addresses regulatory compliance verification in submitted design documents rather than the broader public welfare principle.
Engineer A used AI-assisted drafting tools to generate preliminary design documents for groundwater infrastructure modifications and submitted those documents to Client W without disclosing the AI tool involvement.
At the time of submitting the engineering design documents to Client W
A machine-generated draft report existed and was available for Engineer A's review.
The open-source AI software produced a draft of the comprehensive contaminant report based on the data and inputs provided by Engineer A.
Undetected errors in dimensions and omission of safety features required by local regulations
Insufficient Oversight of Design Documents State
Adapt the AI-generated plans to the site context and prepare them for submission without expending the full review effort applied to the report
Safety feature validation for groundwater infrastructure
Engineer A performed only a cursory review of the AI-generated engineering design documents, adjusting certain elements to align with site-specific conditions but not conducting a comprehensive verification of dimensions, safety features, or regulatory compliance.
A machine-generated draft of the design documents existed and was available for Engineer A's review, containing unverified technical specifications.
The open-source AI software produced draft groundwater infrastructure design documents, including dimensions and safety feature specifications, based on inputs provided by Engineer A.