Step 4: Case Synthesis

Build a coherent case model from extracted entities

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Use of Artificial Intelligence in Engineering Practice
Step 4 of 5
Four-Phase Synthesis Pipeline
1
Entity Foundation
Passes 1-3
2
Analytical Extraction
2A-2E
3
Decision Synthesis
E1-E3 + LLM
4
Narrative
Timeline + Scenario
Phase 1 Entity Foundation
138 entities
Pass 1: Contextual Framework
  • 11 Roles
  • 26 States
  • 8 Resources
Pass 2: Normative Requirements
  • 21 Principles
  • 16 Obligations
  • 11 Constraints
  • 13 Capabilities
Pass 3: Temporal Dynamics
  • 32 Temporal Dynamics
Phase 2 Analytical Extraction
2A: Code Provisions 9
LLM detect algorithmic linking Case text + Phase 1 entities
I.1. Hold paramount the safety, health, and welfare of the public.
I.2. Perform services only in areas of their competence.
I.5. Avoid deceptive acts.
II.1.c. 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 ...
II.2.a. Engineers shall undertake assignments only when qualified by education or experience in the specific technical fields involved.
II.2.b. 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 doc...
III.3. Engineers shall avoid all conduct or practice that deceives the public.
III.8.a. Engineers shall conform with state registration laws in the practice of engineering.
III.9. Engineers shall give credit for engineering work to those to whom credit is due, and will recognize the proprietary interests of others.
2B: Precedent Cases 2
LLM extraction Case text
BER Case 90-6 supporting
linked
It is ethical for an engineer to sign and seal documents prepared using computer-assisted drafting and design (CADD) systems, whether prepared by the engineer or others under their direction and control, so long as the technology is not used as a substitute for engineering judgment.
BER Case 98-3 distinguishing
linked
Technology such as CD-ROM design tools may have an important place in engineering practice, but it must never replace or substitute for engineering judgment; engineers must not offer services in areas where they lack competence merely because a technological tool enables them to do so.
2C: Questions & Conclusions 19 24
Board text parsed LLM analytical Q&C LLM Q-C linking Case text + 2A provisions
Questions (19)
Question_1 Was Engineer A’s use of AI to create the report text ethical, given that Engineer A thoroughly checked the report?
Question_2 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 ...
Question_3 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?
Question_101 Did Engineer A's act of inputting Client W's confidential groundwater and site data into an open-source AI platform constitute an unauthorized disclos...
Question_102 Why did Engineer A apply thorough verification to the report but only a cursory review to the engineering design documents, and does this disparity su...
Question_103 Should Engineer A have disclosed to Client W that the AI drafting tool being used for both the report and the design documents was new to the market a...
Question_104 Given that Engineer B's retirement left Engineer A without a mentor for quality assurance review, did Engineer A have an obligation to secure an alter...
Question_201 How should the Confidentiality of Client W Information be balanced against the practical efficiency gains of using open-source AI tools that require i...
Question_202 Does Review Adequacy of AI Output conflict with Responsible Charge over AI-Assisted Design when Engineer A applied rigorous verification to the report...
Question_203 How should Transparency of AI Contribution be balanced against Intellectual Honesty in AI Text and Credit Attribution in AI Report when no professiona...
Question_204 How should Professional Judgment Primacy in AI Use be reconciled with Public Welfare in AI Design Errors, given that Engineer A's own professional jud...
Question_301 From a deontological perspective, did Engineer A fulfill the duty of confidentiality under II.1.c by inputting Client W's proprietary information into...
Question_302 From a consequentialist perspective, does the fact that Client W ultimately found the draft report satisfactory justify Engineer A's undisclosed and u...
Question_303 Did Engineer A act with professional integrity, in the virtue-ethical sense, when conducting only a cursory review of the AI-generated design document...
Question_304 From a deontological perspective, did Engineer A satisfy the duty of maintaining responsible charge required before sealing engineering documents whos...
Question_401 If Engineer A had not thoroughly cross-checked the AI-generated report against professional journal articles and search-engine queries, would the Boar...
Question_402 If Engineer B had remained available to provide mentorship and quality-assurance review, would the Board still have concluded that Engineer A failed t...
Question_403 If Client W had not discovered the misaligned dimensions and omitted safety features in the AI-generated design documents, would the Board still have ...
Question_404 If Engineer A had not input Client W's information into the open-source AI software, would the Board still have found a confidentiality problem in Eng...
Conclusions (24)
Conclusion_1 Engineer A's use of AI in report writing was partly ethical, and partly unethical. Engineer A was competent and did thoroughly review and verify the A...
Conclusion_2 The use of AI-assisted drafting tools by Engineer A was not unethical per se. However, Engineer A’s misuse of the tool, by failing to maintain Respons...
Conclusion_3 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...
Conclusion_101 The Board's finding that Engineer A failed to obtain client permission to disclose private information can be extended: the confidentiality violation ...
Conclusion_102 The Board's responsible-charge finding can be deepened by noting an internal inconsistency in Engineer A's conduct: applying rigorous, multi-source ve...
Conclusion_103 Beyond the Board's transparency recommendation, a distinct obligation exists that does not depend on resolving the broader AI-disclosure debate: under...
Conclusion_104 The Board's analysis does not directly address whether Engineer A's loss of mentor-based quality assurance created an independent obligation to secure...
Conclusion_201 Q101: The unauthorized disclosure occurred at the moment Engineer A entered Client W's confidential groundwater and site data into the open-source AI ...
Conclusion_202 Q102: The disparity between Engineer A's thorough review of the report and cursory review of the sealed design documents suggests a misapplication of ...
Conclusion_203 Q103/Q104: Beyond any general AI-disclosure question, Engineer A's competence obligations under I.2 and II.2.a arguably required flagging to Client W ...
Conclusion_204 Q201: The tension between confidentiality and efficiency should be resolved in favor of confidentiality as a threshold constraint, not a factor to be ...
Conclusion_205 Q202/Q204: Yes, a genuine conflict exists. Engineer A's own professional judgment determined that a high-level review of the design documents was suff...
Conclusion_206 Q203: Even absent a formal citation standard for AI-generated text, the fact that Client W perceived the report as written by two different authors is...
Conclusion_207 Q301: From a strict deontological reading of II.1.c, Engineer A did not fulfill the confidentiality duty. The duty not to reveal client facts or data ...
Conclusion_208 Q302: A consequentialist framing focused solely on Client W's satisfaction is insufficient to justify Engineer A's undisclosed and initially unverifie...
Conclusion_209 Q303: Viewed through a virtue-ethics lens, Engineer A's cursory review before sealing the design documents reflects a deficiency in professional prude...
Conclusion_210 Q304: Under a deontological view of responsible charge, Engineer A did not satisfy the duty because responsible charge requires the sealing engineer t...
Conclusion_211 Q401: Had Engineer A not cross-checked the AI-generated report against professional journal articles and search-engine queries, the Board would very l...
Conclusion_212 Q402: Even if Engineer B had remained available for mentorship, the Board would likely still have found a responsible charge lapse, because the duty t...
Conclusion_213 Q403: The Board's conclusion regarding misuse of the AI tool and lapse in responsible charge rests on the adequacy of Engineer A's review process, not...
Conclusion_214 Q404: The confidentiality problem identified by the Board is specifically tied to the act of inputting Client W's proprietary groundwater and site dat...
Conclusion_301 The case reveals that Review Adequacy of AI Output is not a fixed standard but must scale with the risk and finality of the work product. Engineer A a...
Conclusion_302 Confidentiality of Client W Information and the practical efficiency of using an open-source AI tool were never actually balanced, because Engineer A ...
Conclusion_303 The Board's treatment of Transparency of AI Contribution as aspirational rather than mandatory, absent a governing citation standard, illustrates a br...
2D: Transformation Classification
stalemate 62%
LLM classification Phase 1 entities + 2C Q&C

Engineer A is caught within a single, unchanging set of rules where several codified duties pull in different directions at once: the duty to serve the client efficiently conflicts with the non-negotiable confidentiality duty (C11, C23); the duty to exercise professional judgment conflicts with the duty to calibrate review to public-safety risk (C12, C22); and the emerging expectation of transparency conflicts with the fact that 'no universal guideline mandating AI disclosure' currently exists (C3, C24). Rather than resolving which obligation prevails, the Board's conclusions largely restate that both obligations remain valid and unresolved, consistent with the stalemate pattern of stakeholders unable to exit the rule-set.

Reasoning

The Board's resolution does not shift the ethical duties to a new party (no transfer), nor does it show duties cycling between actors over project phases (no oscillation); instead, Engineer A remains 'trapped' between multiple simultaneously valid but incompatible obligations—confidentiality vs. efficiency, professional-judgment autonomy vs. risk-calibrated review, and transparency vs. the absence of a governing citation/disclosure standard. The Board explicitly declines to definitively rank these competing duties, leaving the tensions acknowledged but unresolved.

2E: Rich Analysis (Causal Links, Question Emergence, Resolution Patterns)
LLM batched analysis label-to-URI resolution Phase 1 entities + 2C Q&C + 2A provisions
Causal-Normative Links (7)
CausalLink_AI Tool Adoption Because AI Tool Adoption was guided by the obligation to perform services only in areas of competence yet proceeded without adequately verifying that ...
CausalLink_Revision Instruction Revision Instruction is guided by the duty to hold paramount public safety, health, and welfare, which matters because it is the corrective response t...
CausalLink_Confidential Data Input Confidential Data Input violates Client Confidentiality precisely because it directly causes Confidential Information Exposure, illustrating how the c...
CausalLink_Thorough Report Review Thorough Report Review fulfills the Direction and Control obligation and is guided by the duties to avoid deceptive acts and to work only within compe...
CausalLink_Report Sealing and Submission Report Sealing and Submission violates the duty to Give Credit for Engineering Work because sealing the report as the engineer's own product while omi...
CausalLink_Cursory Design Review Because Engineer A's cursory review of AI-generated designs directly caused the Design Error Discovery that later forced Client W's Revision Instructi...
CausalLink_Design AI Disclosure Omission Since this omission sits alongside the AI-driven design and reporting process that produced the inconsistencies Engineer A later noticed, failing to d...
Question Emergence (19)
QuestionEmergence_1 The question arises because Engineer A's thorough review satisfies competence and accuracy obligations, yet this same act of using AI-generated text w...
QuestionEmergence_2 The question arises because Engineer A substituted a cursory, high-level check for the kind of independent, detailed review normally expected of the e...
QuestionEmergence_3 The question arose because Engineer A's undisclosed use of AI in report and design work forced a comparison between traditional credit attribution nor...
QuestionEmergence_4 The question emerged because the physical act of data disclosure and the subsequent verification of AI output are treated as separable events under di...
QuestionEmergence_5 The question arises because Engineer A's differing levels of diligence across two AI-generated deliverables expose an unresolved prioritization betwee...
QuestionEmergence_6 The question arises because Engineer A's use of an untested, unfamiliar AI tool created a gap between what the client was told (general AI use) and wh...
QuestionEmergence_7 The question arose because the loss of an established mentorship based QA process coincided with adoption of a new and unverified technology, creating...
QuestionEmergence_8 The question arises because Engineer A's data input action satisfies an efficiency-oriented professional practice norm while simultaneously violating ...
QuestionEmergence_9 The question arises because identical AI unfamiliarity produced inconsistent review rigor across two deliverables from the same engineer, exposing an ...
QuestionEmergence_10 The question arose because Engineer A used an AI tool to draft polished report text without informing Client W, and the resulting stylistic mismatch c...
QuestionEmergence_11 The question arises because the same act, Engineer A's approval of a cursory review, is the evidence used both to invoke professional autonomy and to ...
QuestionEmergence_12 The question emerged because Engineer A's unilateral act of feeding Client W's confidential information into an AI system created an unresolved confli...
QuestionEmergence_13 The question arose because the case data separates process (undisclosed AI drafting) from outcome (client satisfaction), forcing a choice between judg...
QuestionEmergence_14 The question arises because sealing a design with only cursory review creates a visible gap between the professional's certifying authority and the ac...
QuestionEmergence_15 The question arises because sealing engineering documents traditionally presumes the sealer possesses full command of the underlying content, but Engi...
QuestionEmergence_16 The question arises because the Board's partial ethicality finding rests on an empirical fact, the cross-checking behavior, that could have been other...
QuestionEmergence_17 The question arises because Engineer B's retirement removed a review mechanism at the same time Engineer A committed a cursory review of AI output, cr...
QuestionEmergence_18 The question arises because the Board's actual finding depended on facts discovered after Engineer A's review (the omitted safety features and dimensi...
QuestionEmergence_19 This question arose because the case record bundles the confidentiality breach (Confidential Data Input) and the AI drafting practice (AI Report Gener...
Resolution Patterns (24)
ResolutionPattern_1 Given that Engineer A verified the AI-generated report's accuracy but separately failed to secure client consent for disclosed data and omitted citati...
ResolutionPattern_2 Given that Engineer A relied on an unfamiliar AI tool for sealed design documents but reviewed them only at a high level without replacing the lost me...
ResolutionPattern_3 Given that no universal standard required AI disclosure and Engineer A's tool use resembled ordinary software assistance, the board found no strict et...
ResolutionPattern_4 Given that Engineer A uploaded Client W's confidential data to an open platform of unknown retention practices without consent, the board concluded th...
ResolutionPattern_5 Given that Engineer A devoted greater verification effort to the report than to the higher-stakes sealed design documents from the same unfamiliar AI ...
ResolutionPattern_6 Given that Client W could perceive a shift in authorial voice from the AI-drafted introduction, the board concluded that III.9's credit-attribution du...
ResolutionPattern_7 Because Engineer B's retirement removed the customary human check on Engineer A's work, the board reasoned that reliance on an unfamiliar AI tool as a...
ResolutionPattern_8 Because Engineer A uploaded Client W's confidential site data to an open-source AI platform without consent, the board concluded the confidentiality v...
ResolutionPattern_9 Given that Engineer A devoted rigorous review to the lower-risk draft report but only a high-level check to the higher-risk sealed design documents, t...
ResolutionPattern_10 Because the drafting tool was untested and Engineer A lacked mentor support after Engineer B's retirement, the board concluded that competence obligat...
ResolutionPattern_11 Because Engineer A could not verify how the open-source AI platform would store or reuse Client W's proprietary groundwater data, and no consent was s...
ResolutionPattern_12 Given that Engineer A's own judgment deemed a cursory review sufficient yet that same review failed to catch misaligned dimensions and missing safety ...
ResolutionPattern_13 Even though no professional citation standard specifically addressed AI-generated text, Client W's observation that the report read as though written ...
ResolutionPattern_14 Applying a strict deontological lens, the board concluded that the duty not to reveal client data without consent was violated the instant Engineer A ...
ResolutionPattern_15 Because Client W's satisfaction was the only outcome considered in the narrow consequentialist argument, the board found this insufficient, reasoning ...
ResolutionPattern_16 Given that Engineer A's review was only cursory and the sealed documents came from an unfamiliar AI tool, the board concluded this reflects a deficien...
ResolutionPattern_17 Because Engineer A only made superficial adjustments to unfamiliar AI output rather than substantively verifying it, the board concluded the deontolog...
ResolutionPattern_18 Because Engineer A actually cross-checked the AI-generated report against journal articles and search queries, the board concluded this verification s...
ResolutionPattern_19 Even hypothesizing that Engineer B remained available, the board concluded a responsible charge lapse would still be found, because informal mentor re...
ResolutionPattern_20 Because the board's evaluation rests on the adequacy of Engineer A's review process rather than on whether Client W happened to catch the errors, it c...
ResolutionPattern_21 Given that the confidentiality violation was traced specifically to the proprietary content Engineer A uploaded, the board concluded that substituting...
ResolutionPattern_22 Because Engineer A trusted the same unfamiliar AI tool equally despite the design documents carrying far greater public safety consequences than the d...
ResolutionPattern_23 Because Engineer A never paused to assess the AI tool's data practices or secure client consent before inputting proprietary information, the board co...
ResolutionPattern_24 Given that the current Code contains no rule requiring AI disclosure, the board concluded that Engineer A's uncited use remained technically compliant...
Phase 3 Decision Point Synthesis
Decision Point Synthesis (E1-E3 + Q&C Alignment + LLM)
E1-E3 algorithmic Q&C scoring LLM refinement Phase 1 entities + 2C Q&C + 2E rich analysis
E1
Obligation Coverage
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E2
Action Mapping
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E3
Composition
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Q&C
Alignment
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LLM
Refinement
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Phase 4 Narrative Construction
Narrative Elements (Event Calculus + Scenario Seeds)
algorithmic base LLM enhancement Phase 1 entities + Phase 3 decision points
4.1
Characters
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4.2
Timeline
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4.3
Conflicts
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4.4
Decisions
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Review Entities & Commit View Synthesis

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