Step 4: Case Synthesis

Build a coherent case model from extracted entities

Back to Case
Pipeline Steps
Overview
1 Contextual Framework
2 Normative Requirements
3 Temporal Dynamics Reconcile
4 Whole-Case Synthesis
Extraction Review Full View
Provenance
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
191 entities
Pass 1: Contextual Framework
  • 9 Roles
  • 29 States
  • 7 Resources
Pass 2: Normative Requirements
  • 27 Principles
  • 27 Obligations
  • 26 Constraints
  • 28 Capabilities
Pass 3: Temporal Dynamics
  • 38 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 analogizing
linked
It is ethical for an engineer to sign and seal documents created using a CADD system, whether prepared by the engineer themselves or by other engineers working under their direction and control, provided the engineer has the requisite background, education, and training to be proficient with the technology and its limitations.
BER Case 98-3 analogizing
linked
It is unethical for an engineer to offer services using new technology in areas where they lack experience or competency, and technology must never be a replacement or substitute for engineering judgment; engineers must acknowledge significant contributions by others.
2C: Questions & Conclusions 21 26
Board text parsed LLM analytical Q&C LLM Q-C linking Case text + 2A provisions
Questions (21)
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 By uploading Client W's confidential site data and groundwater monitoring information into an open-source AI platform without obtaining Client W's pri...
Question_102 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 s...
Question_103 Because the AI-generated report produced stylistically inconsistent text that Client W noticed and attributed to what appeared to be two different aut...
Question_104 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 ...
Question_201 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 w...
Question_202 Does the principle of Engineer A AI Disclosure Transparency—which the Board acknowledged is ethically favored when AI plays a substantial role—conflic...
Question_203 Does the principle of Engineer A Responsible Charge Design Failure—arising from Engineer A's cursory review of AI-generated plans that contained misal...
Question_204 Does the principle of Engineer A Client Data AI Upload—which reflects the engineer's interest in leveraging available tools to meet client deliverable...
Question_301 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 rep...
Question_302 From a deontological perspective, did Engineer A violate a categorical duty to protect client confidentiality by uploading Client W's proprietary site...
Question_303 From a consequentialist perspective, did the aggregate outcomes of Engineer A's AI-assisted workflow — including the polished but stylistically incons...
Question_304 From a virtue ethics perspective, did Engineer A demonstrate the professional integrity and intellectual honesty expected of a licensed engineer by ch...
Question_305 From a virtue ethics perspective, does Engineer A's decision to apply their professional seal to AI-generated design documents after only a cursory re...
Question_306 From a consequentialist perspective, would a universal norm requiring engineers to disclose AI involvement whenever AI plays a substantial role in gen...
Question_401 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 ...
Question_402 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 a...
Question_403 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-generat...
Question_404 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...
Conclusions (26)
Conclusion_1 Engineer A's use of AI in report writing was partly ethical, and partly unethical.
Conclusion_2 The use of AI-assisted drafting tools by Engineer A was not unethical per se.
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 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 e...
Conclusion_102 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 mee...
Conclusion_103 The Board's conclusion that Engineer A had no professional or ethical obligation to disclose AI use in the report—by analogy to conventional engineeri...
Conclusion_104 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 engine...
Conclusion_105 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 f...
Conclusion_106 The Board's discretionary approach to AI disclosure—treating it as ethically favored but not categorically required absent contractual obligation—prod...
Conclusion_201 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 w...
Conclusion_202 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...
Conclusion_203 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 'rea...
Conclusion_204 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 E...
Conclusion_205 In response to Q201: A genuine and irresolvable tension exists between the principle of technical accuracy achieved through thorough review and the pr...
Conclusion_206 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 inte...
Conclusion_207 In response to Q301 and Q302: From a deontological perspective, Engineer A failed two categorical duties simultaneously. First, the duty of candor to ...
Conclusion_208 In response to Q303: From a consequentialist perspective, the aggregate outcomes of Engineer A's AI-assisted workflow produce a net harm that substant...
Conclusion_209 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 th...
Conclusion_210 In response to Q306: A universal norm requiring engineers to disclose AI involvement whenever AI plays a substantial role in generating a work product...
Conclusion_211 In response to Q401: Prior disclosure to Client W and explicit written consent for uploading confidential site data to an open-source AI platform woul...
Conclusion_212 In response to Q402: Had Engineer A engaged a qualified substitute reviewer to replace Engineer B's mentorship and quality assurance role before submi...
Conclusion_213 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 applie...
Conclusion_214 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 a...
Conclusion_301 The most consequential unresolved tension in this case is between Engineer A AI Report Competence and Engineer A AI Authorship Concealment. The Board ...
Conclusion_302 The tension between Engineer A Responsible Charge Design Failure and Engineer A Seal on AI Design Documents exposes a structural vulnerability in how ...
Conclusion_303 The interaction among Engineer A Client Data AI Upload, Engineer A Professional Competence AI Use, and Engineer A Mentor Loss Response reveals that wh...
2D: Transformation Classification
stalemate 72%
LLM classification Phase 1 entities + 2C Q&C

Engineer A remains 'trapped in the set of rules': the obligation to use available tools efficiently, the duty of competence, the duty of candor/disclosure, and the duty of confidentiality cannot all be simultaneously satisfied within the current Code framework. The Board resolves some discrete questions (confidentiality breach is clearly unethical) but explicitly leaves the deeper structural tensions standing—producing prioritization guidance (candor over output quality) without dissolving the underlying conflict, and flagging that the conventional-software analogy fails to settle the disclosure question.

Reasoning

The Board's resolution surfaces multiple valid but incompatible obligations that it explicitly declines to fully reconcile—particularly the tension between competence/output-quality (satisfied via thorough review) and candor/authorship-transparency (violated via concealment). Per the framework, a stalemate exists when 'competing duties' coexist and the 'ethical dilemma persists,' which the Board itself acknowledges in C18 by calling the tension 'genuine and irresolvable' and in C11 by noting a tension 'not resolved by the Board so much as it was exposed.'

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 (10)
CausalLink_Project Acceptance Accepting the project within the bounds of competence matters because it establishes the foundational professional commitment to public safety, and an...
CausalLink_AI Tool Adoption for Report Adopting the AI tool for the report fulfils competence obligations by leveraging available technology, but the causal chain shows it directly produces...
CausalLink_AI Tool Adoption for Design Using AI for design generation under dual deliverable pressure carries normative weight because the causal chain produces AI-generated design outputs ...
CausalLink_Client Data Upload to AI Uploading client data to the AI tool violates confidentiality because it causally produces client data exposure to a third-party system, and this brea...
CausalLink_Thorough Report Review Conducting a thorough review fulfils both direction and control over the work product and competence obligations, and this matters causally because th...
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 abo...
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 mat...
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 t...
CausalLink_AI Disclosure Omission for Des
CausalLink_Design Document Submission Submitting the design document without adequate review or qualification fulfils no protective obligation and instead places a potentially defective de...
Question Emergence (21)
QuestionEmergence_1 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 l...
QuestionEmergence_2 This question arose because Engineer A's AI-assisted workflow produced multiple distinct categories of harm across a single project, and consequential...
QuestionEmergence_3 This question emerged because Engineer A sealed and submitted AI-generated work products to Client W without any acknowledgment of AI involvement in e...
QuestionEmergence_4 This question arose because the data simultaneously supports two defensible positions: that AI tools are legitimate engineering aids when an engineer ...
QuestionEmergence_5 The question emerged because Engineer A produced client deliverables substantially through AI tools and then omitted any mention of that fact, placing...
QuestionEmergence_6 The question arose because Engineer A's thorough review satisfies the professional competence and responsible charge obligations that normally govern ...
QuestionEmergence_7 This question arose because Engineer A's decision to input Client W's confidential data into an open-source AI platform created a potential confidenti...
QuestionEmergence_8 The question emerged because Engineer A's state of unfamiliarity with the AI drafting tool, combined with the loss of mentor oversight after Superviso...
QuestionEmergence_9 This question arose because Client W's direct observation of stylistic inconsistency transformed what might otherwise be a silent omission into a situ...
QuestionEmergence_10 This question arose because Engineer B's retirement created a foreseeable structural change in Engineer A's quality assurance capacity at the same mom...
QuestionEmergence_11 This question emerged because the Board's competence finding and the concealment finding were reached independently, leaving open whether satisfying o...
QuestionEmergence_12 The question emerged because Engineer A's conduct simultaneously satisfied the factual conditions that activate both warrants: the AI contribution was...
QuestionEmergence_13 This question emerged because the professional seal is designed to function as a guarantee of responsible charge, but Engineer A's cursory review of A...
QuestionEmergence_14 This question emerged because Engineer A's single act of uploading confidential client data to an unfamiliar AI tool simultaneously activated two dist...
QuestionEmergence_15 This question emerged because Engineer B's retirement created a genuine competence gap at the same moment Engineer A faced dual deliverable pressure, ...
QuestionEmergence_16 This question arose because the professional seal is the institutional symbol of responsible stewardship, and Engineer A's cursory review created a di...
QuestionEmergence_17 This question emerged because the actual harms produced by Engineer A's undisclosed AI use, including design defects, public safety risks, and client ...
QuestionEmergence_18 This question arose because the Board identified multiple distinct violations, and it is genuinely unclear whether a single procedural act, prior writ...
QuestionEmergence_19 This question arose because the Board's findings identified failures across multiple dimensions simultaneously, including design deficiencies, regulat...
QuestionEmergence_20 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 demonstr...
QuestionEmergence_21 This question arose because the stylistic inconsistency noticed by Client W created observable evidence that the work product had a non-uniform origin...
Resolution Patterns (26)
ResolutionPattern_1 Because Engineer A exposed Client W's confidential data to a system with unknown retention and access policies before obtaining any consent, the board...
ResolutionPattern_2 Because Engineer A had no experience with the AI tool and because the tool's probabilistic output characteristics created a verification burden that c...
ResolutionPattern_3 Because Client W could detect the stylistic discontinuity between AI-generated and human-written sections, Engineer A's silence about AI involvement f...
ResolutionPattern_4 Because the design review was cursory rather than substantive, and because the sealed documents contained errors that a competent review would have ca...
ResolutionPattern_5 Because Engineer B's retirement removed a structural component of Engineer A's quality assurance process and because Engineer A's substitution of an u...
ResolutionPattern_6 Given that no mandatory disclosure rule currently exists and the Board analogized AI tools to conventional engineering software, the Board concluded t...
ResolutionPattern_7 Because Engineer A exposed Client W's confidential data to an uncontrolled platform without consent, the Board found an independent ethical breach und...
ResolutionPattern_8 Because Engineer A used an untested, unfamiliar tool on a live engagement without any prior validation or verification protocol, and because the resul...
ResolutionPattern_9 Because Client W could perceive the stylistic inconsistency caused by AI authorship and Engineer A withheld the explanation, the Board concluded that ...
ResolutionPattern_10 Because the Board found the report technically competent but simultaneously found the concealment of AI authorship perceptible and materially misleadi...
ResolutionPattern_11 Because Engineer A reviewed the AI-generated design documents only at a high level and the documents contained misaligned dimensions and omitted requi...
ResolutionPattern_12 Because Engineer A's single decision to adopt an AI tool compounded three distinct ethical failures that each independently violated a code obligation...
ResolutionPattern_13 Because Engineer A thoroughly checked the AI-generated report and verified its technical content before submission, the board found that the use of AI...
ResolutionPattern_14 Because the board found that AI-assisted drafting is not categorically different from other engineering software tools when the engineer exercises ade...
ResolutionPattern_15 Because no contractual or regulatory requirement compelled disclosure and the board analogized AI drafting tools to conventional software, it conclude...
ResolutionPattern_16 Because Client W could detect a stylistic discontinuity consistent with multiple authors, and because Engineer A offered no explanation, the board fou...
ResolutionPattern_17 Because Engineer A simultaneously lost their mentor, accepted complex sealed deliverables, and adopted an unfamiliar AI tool without any substitute pe...
ResolutionPattern_18 Because Engineer A's thorough review addressed only output quality and left the authorship misrepresentation intact, the board found that satisfying t...
ResolutionPattern_19 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 misrepresen...
ResolutionPattern_20 Because Engineer A acted without client consent on the data upload and without disclosure on the authorship question, the board found two independent ...
ResolutionPattern_21 Because Engineer A was unfamiliar with the tool and reviewed the design documents only at a high level, the efficiency gains were achieved by offloadi...
ResolutionPattern_22 Because Engineer A chose technological substitution over qualified human oversight after losing Engineer B, and then sealed documents containing detec...
ResolutionPattern_23 Because AI language models produce content through probabilistic processes that differ fundamentally from deterministic engineering software, and beca...
ResolutionPattern_24 Because Engineer A's violations included both the unauthorized data upload and the independent failures of competence and responsible charge over the ...
ResolutionPattern_25 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...
ResolutionPattern_26 Given that the Board's distinction between the report and the design documents rested substantially on the difference in review depth rather than on t...
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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