Step 4 Review - Case 7

Step 4: Synthesis Review

Case 7: Use of Artificial Intelligence in Engineering Practice

Back to Step 4

161

Entities

9

Provisions

15

Questions

9

Conclusions

Stalemate

Transformation

Stalemate Competing obligations remain in tension without clear resolution

Engineer A faces persistent tension between the Confidentiality_AICase_Upload principle (violated by uploading client data to open-source AI) and the Competence_AICase_Canon principle (violated by using unfamiliar AI tools), with the Board acknowledging both violations without providing definitive guidance on how to resolve such conflicts in future similar situations.

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Synthesis Reasoning Flow

Shows how NSPE provisions inform questions and conclusions - the board's reasoning chain

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Nodes:

■ NSPE Provisions ◆ Questions ▬ Conclusions ▬ Entities (labels)

Edge Colors:

→ Provision informs Question
→ Question answered by Conclusion
→ Provision applies to Entity

NSPE Code Provisions Referenced

View Extraction

I.1. I.1.

Full Text:

Hold paramount the safety, health, and welfare of the public.

Relevant Case Excerpts:

From discussion:
"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”. Engineer A’s oversight of engineering plans was inadequate, raising ethical concerns."
Confidence: 95.0%

Applies To:

role Engineer A

This provision governs Engineer A's duty to ensure public safety when using AI tools that could affect groundwater monitoring and safety features

state EngineerA_NonCompliant_DesignDocuments

This provision addresses the state where design documents may lack required safety features due to AI limitations

principle PublicWelfare_SafetyOmission_Facts

This provision embodies the principle that safety omissions violate the paramount duty to public welfare

principle PublicWelfare_AICase_Canon

This provision embodies the canonical principle of holding public welfare paramount in AI usage cases

obligation EngineerA_Safety_PublicWelfare

This provision specifies Engineer A's obligation to prioritize safety and public welfare above all else

constraint Safety_Feature_Inclusion_Boundary

This provision creates the constraint that all required safety features must be included in designs

I.2. I.2.

Full Text:

Perform services only in areas of their competence.

Relevant Case Excerpts:

From discussion:
"Culminating in the key question: Is using AI adding a new tool to an engineer’s toolbox, or is it something more? Fundamental Canon I.2 states that engineers “perform services only in areas of their competence” and Code section II.2.a states that engineers must “undertake assignments only when qualified by education or experience in"
Confidence: 95.0%

Applies To:

role Engineer A

This provision governs Engineer A's requirement to only perform services within competence, including AI tool usage

state EngineerA_AIFirstUse_OpenSourceAI

This provision addresses the state where Engineer A uses AI tools for the first time without prior experience

state EngineerA_TechnicalWritingInsecurity

This provision relates to Engineer A's lack of confidence in technical writing competence

principle Competence_AIUsage_Facts

This provision embodies the principle that competence is required for AI usage in engineering

principle Competence_TechnicalWriting_Facts

This provision embodies the principle requiring competence in technical writing tasks

principle Competence_AICase_Canon

This provision embodies the canonical principle of competence requirements in AI usage cases

obligation EngineerA_Competence_AI_Tools

This provision specifies Engineer A's obligation to be competent in AI tools before using them

constraint AI_Tool_Experience_Limitation

This provision relates to the constraint of Engineer A's limited experience with AI tools

constraint Technical_Writing_Competence

This provision relates to the constraint of technical writing competence requirements

I.5. I.5.

Full Text:

Avoid deceptive acts.

Relevant Case Excerpts:

From discussion:
"Fundamental Canon I.5 requires an Engineer to “avoid deceptive acts,” which was not violated here."
Confidence: 95.0%

Applies To:

role Engineer A

This provision governs Engineer A's duty to avoid deceptive acts by not disclosing AI usage

state EngineerA_InsufficientAttribution_AIContent

This provision addresses the deceptive state of not attributing AI-generated content

state EngineerA_InsufficientAttribution_AIContributions

This provision addresses the deceptive state of not properly attributing AI contributions

principle Transparency_NonDisclosure_Facts

This provision embodies the principle that non-disclosure of AI usage lacks transparency

principle Integrity_AICase_Deception

This provision embodies the principle that concealing AI usage constitutes deception

action Non-disclosure AI Usage

This provision prohibits the deceptive action of not disclosing AI usage to the client

II.1.c. II.1.c.

Full Text:

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.

Relevant Case Excerpts:

From discussion:
"rmed a thorough review and cross-checked the work on the report, much like Engineer A would have likely done if the report had been initially drafted by an engineer intern or other support staff. Per Code section II.1.c, confidential information can only be shared with prior consent of the Client."
Confidence: 95.0%

Applies To:

role Engineer A

This provision governs Engineer A's duty to protect client confidentiality when using AI tools

state ClientW_PublicDomainExposure_AIInterface

This provision addresses the state where client data may be exposed through AI interfaces

resource Client_W_Information_Package

This provision requires protection of the client's confidential information package

resource Site_Groundwater_Monitoring_Data_Year

This provision requires protection of confidential site monitoring data

principle Confidentiality_AICase_Upload

This provision embodies the principle that uploading client data to AI violates confidentiality

obligation EngineerA_Confidentiality_ClientW

This provision specifies Engineer A's obligation to maintain Client W's confidentiality

constraint Client_W_Confidentiality_Boundary

This provision creates the constraint on sharing client data with AI systems

action Confidential Data Upload

This provision prohibits the action of uploading confidential client data to AI systems

II.2.a. II.2.a.

Full Text:

Engineers shall undertake assignments only when qualified by education or experience in the specific technical fields involved.

Relevant Case Excerpts:

From discussion:
"the key question: Is using AI adding a new tool to an engineer’s toolbox, or is it something more? Fundamental Canon I.2 states that engineers “perform services only in areas of their competence” and Code section II.2.a states that engineers must “undertake assignments only when qualified by education or experience in the specific technical fields involved.” Here, Engineer A, as an experienced environmental engineer"
Confidence: 95.0%

Applies To:

role Engineer A

This provision governs Engineer A's requirement to be qualified in AI tool usage before undertaking such assignments

state EngineerA_AIFirstUse_OpenSourceAI

This provision addresses undertaking AI-assisted work without prior qualification

principle Competence_AIUsage_Facts

This provision embodies the principle requiring qualification for AI usage assignments

obligation EngineerA_Tool_Familiarity

This provision specifies the obligation to be familiar with tools before using them

constraint AI_Tool_Experience_Limitation

This provision relates to the constraint of lacking AI tool experience

II.2.b. II.2.b.

Full Text:

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.

Relevant Case Excerpts:

From discussion:
"rformed 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, “[e]ngineers shall not affix their signatures to any plans or documents ."
Confidence: 90.0%

From discussion:
"ngineer 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. Code section II.2.b states that, “[e]ngineers 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 di"
Confidence: 95.0%

Applies To:

role Engineer A

This provision governs Engineer A's signature on AI-assisted documents and maintaining control over them

state EngineerA_CompromisedOversight_WithoutEngineerB

This provision addresses the compromised oversight state after losing mentor support

state EngineerA_InadequateReview_AIDesignDocuments

This provision addresses inadequate review of AI-generated design documents

principle Integrity_SealApplication_Facts

This provision embodies the principle of integrity in applying professional seals

principle ResponsibleCharge_AICase_Violation

This provision embodies the principle that AI usage can violate responsible charge requirements

principle ProfessionalJudgment_AICase_Substitute

This provision embodies the principle that AI cannot substitute for professional judgment

obligation EngineerA_Responsible_Charge

This provision specifies the obligation to maintain responsible charge over documents

obligation EngineerA_ResponsibleCharge_Design

This provision specifies the obligation to maintain responsible charge over design documents

constraint Responsible_Charge_Maintenance

This provision creates the constraint of maintaining responsible charge when using AI

action Professional Seal Application

This provision governs the action of applying professional seals to AI-assisted documents

action Cursory Design Review

This provision prohibits cursory review when documents require responsible charge

III.3. III.3.

Full Text:

Engineers shall avoid all conduct or practice that deceives the public.

Applies To:

role Engineer A

This provision governs Engineer A's conduct in avoiding deception about AI usage

principle Transparency_NonDisclosure_Facts

This provision embodies the principle that non-disclosure deceives the public

principle Integrity_AICase_Deception

This provision embodies the principle that concealing AI usage is deceptive conduct

obligation EngineerA_AI_Disclosure_ClientW

This provision relates to the obligation to disclose AI usage to avoid deception

action Non-disclosure AI Usage

This provision prohibits the deceptive practice of not disclosing AI usage

III.8.a. III.8.a.

Full Text:

Engineers shall conform with state registration laws in the practice of engineering.

Relevant Case Excerpts:

From discussion:
"Engineer A did not maintain responsible charge in violation of licensure law which violates Code section III.8.a."
Confidence: 95.0%

Applies To:

role Engineer A

This provision requires Engineer A to conform with state registration laws when using AI in practice

resource Local_Safety_Regulations_Site

This provision requires conformance with local safety regulations

obligation EngineerA_Regulatory_Compliance

This provision specifies the obligation to comply with regulatory requirements

constraint Professional_Standard_Compliance

This provision creates the constraint of complying with professional standards

III.9. III.9.

Full Text:

Engineers shall give credit for engineering work to those to whom credit is due, and will recognize the proprietary interests of others.

Relevant Case Excerpts:

From discussion:
"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."
Confidence: 95.0%

From discussion:
"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.” There are currently no universal guidelines mandating AI"
Confidence: 90.0%

Applies To:

role Engineer A

This provision requires Engineer A to give credit to AI tools and recognize their proprietary nature

state EngineerA_InsufficientAttribution_AIContent

This provision addresses the failure to properly attribute AI-generated content

state EngineerA_InsufficientAttribution_AIContributions

This provision addresses insufficient attribution of AI contributions to work

resource OpenSource_AI_ReportDrafting_Software

This provision requires recognition of the proprietary interests in AI software

resource AI_Assisted_Design_Drafting_Tools

This provision requires giving credit for AI-assisted design tools' contributions

principle Transparency_AICase_Attribution

This provision embodies the principle of transparent attribution in AI usage

obligation EngineerA_Attribution_AI_Report

This provision specifies the obligation to attribute AI contributions to reports

obligation EngineerA_Attribution_AI_Design

This provision specifies the obligation to attribute AI contributions to designs

obligation EngineerA_Attribution_AI_Sources

This provision specifies the obligation to attribute AI sources properly

constraint AI_Attribution_Requirement

This provision creates the constraint requiring proper AI attribution

Questions & Conclusions

View Extraction

Linked View Side-by-Side

Each question is shown with its corresponding conclusion(s). This reveals the board's reasoning flow.

Question 1 Board Question

Was Engineer A’s use of AI to create the report text ethical, given that Engineer A thoroughly checked the report?

Involves: Engineer A

Board's Conclusion

Conclusion 2 Board Conclusion

Engineer A's use of AI in report writing was partly ethical, and partly unethical.

Question 2 Board Question

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?

Involves: Engineer A

Board's Conclusion

Conclusion 1 Board Conclusion

The use of AI-assisted drafting tools by Engineer A was not unethical per se.

Question 3 Board Question

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?

Involves: Engineer A

Board's Conclusion

Conclusion 3 Board Conclusion

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).

Conclusion 5 Analytical

The Board's conclusion that disclosure is not required 'unless such disclosure is required under Engineer A's contract' establishes a contractual rather than ethical baseline for AI transparency. However, this approach may inadequately address the evolving nature of informed consent in professional services. When AI tools fundamentally alter the nature of professional work products—potentially affecting accuracy, originality, and liability—the traditional contract-based disclosure framework may need supplementation with professional judgment-based disclosure standards.

Cites: I.5. III.3.

Question 4 Implicit

I.2. II.2.a.

Should Engineer A have sought alternative mentorship or quality assurance arrangements before proceeding with AI tools, given their acknowledged technical writing limitations?

Involves: Engineer A Engineer B EngineerA_TechnicalWritingInsecurity EngineerA_MentorLoss_EngineerB_Retirement

Analysis

Conclusion 6 Response

Addressing the implicit question about alternative mentorship arrangements (Q101), Engineer A had several ethically preferable options before resorting to unfamiliar AI tools. These included: seeking temporary mentorship from other qualified professionals, requesting project timeline extensions to allow for skill development, or declining the project while referring Client W to more suitable practitioners. The choice to proceed with AI tools without adequate preparation represents a failure to exhaust reasonable alternatives for maintaining competence, particularly given the public safety implications of groundwater infrastructure design.

Cites: I.1. I.2. II.2.a.

Question 5 Implicit

I.2. II.2.a.

What ethical obligations arise when an engineer uses unfamiliar technology tools in professional practice without adequate training or experience?

Involves: EngineerA_AIFirstUse_OpenSourceAI AI_Tool_Experience_Limitation OpenSource_AI_ReportDrafting_Software

Analysis

Conclusion 4 Analytical

Beyond the Board's finding that Engineer A's AI use was 'partly ethical, and partly unethical,' the case reveals a critical gap in professional development frameworks for emerging technologies. Engineer A's situation—losing mentorship support precisely when facing unfamiliar AI tools—highlights how traditional competence maintenance structures may be inadequate for rapid technological change. The Board's conclusion implicitly recognizes that competence is not binary but contextual, requiring engineers to develop adaptive learning strategies rather than relying solely on established mentorship patterns.

Cites: I.2. II.2.a.

Question 6 Implicit

II.1.c. I.1.

Does the open-source nature of the AI tools create additional ethical considerations regarding data security, intellectual property, and professional liability?

Involves: OpenSource_AI_ReportDrafting_Software AI_Assisted_Design_Drafting_Tools ClientW_PublicDomainExposure_AIInterface

Analysis

Conclusion 5 Analytical

Cites: I.5. III.3.

Question 7 Principle Tension

I.2. II.1.c.

How should Engineer A balance the Competence_AICase_Canon requirement against the Confidentiality_AICase_Upload obligation when AI tools require data input to function effectively?

Involves: Competence_AICase_Canon Confidentiality_AICase_Upload Engineer A Client W

Analysis

Conclusion 8 Synthesis

The case reveals an unresolved tension between the Competence_AICase_Canon and Confidentiality_AICase_Upload principles (Q201). Engineer A faced an impossible choice: either maintain client confidentiality by avoiding AI tools that require data upload, or pursue competence enhancement through AI assistance at the cost of potentially compromising confidential information. The Board's analysis suggests this tension was inadequately resolved, as Engineer A proceeded with data upload without explicit client consent, prioritizing perceived competence over confidentiality protection.

Cites: I.2. II.1.c.

Question 8 Principle Tension

II.2.b. III.9.

Does the ResponsibleCharge_AICase_Violation principle conflict with the Transparency_AICase_Attribution principle when disclosure might undermine client confidence in professional judgment?

Involves: ResponsibleCharge_AICase_Violation Transparency_AICase_Attribution EngineerA_Responsible_Charge EngineerA_Attribution_AI_Report

Analysis

Conclusion 9 Synthesis

The interaction between ResponsibleCharge_AICase_Violation and Transparency_AICase_Attribution principles demonstrates a fundamental challenge in AI-assisted professional practice. Engineer A's failure to adequately disclose AI contributions while maintaining responsible charge creates a paradox: claiming professional responsibility for work products while obscuring the actual sources of professional judgment. This case suggests that responsible charge in the AI era may require enhanced transparency standards to maintain the integrity of professional accountability.

Cites: II.2.b. III.9.

Question 9 Principle Tension

I.2. II.2.a.

How does the ProfessionalJudgment_AICase_Substitute principle interact with the Competence_TechnicalWriting_Facts when an engineer's weakness in one area leads to AI dependency?

Involves: ProfessionalJudgment_AICase_Substitute Competence_TechnicalWriting_Facts EngineerA_TechnicalWritingInsecurity

Analysis

Analysis pending - insufficient information in case to address this question.

Question 10 Theoretical

I.2. II.2.a.

From a deontological perspective, did Engineer A fulfill their categorical duty to maintain competence when using unfamiliar AI tools without adequate preparation?

Involves: Engineer A EngineerA_Competence_AI_Tools AI_Tool_Experience_Limitation

Analysis

Analysis pending - insufficient information in case to address this question.

Question 11 Theoretical

I.1.

From a consequentialist perspective, do the potential benefits of AI-assisted engineering work justify the risks to public welfare and professional integrity identified in this case?

Involves: PublicWelfare_AICase_Canon EngineerA_Safety_PublicWelfare AI_Assisted_Design_Drafting_Tools

Analysis

Analysis pending - insufficient information in case to address this question.

Question 12 Theoretical

I.5. III.3.

From a virtue ethics perspective, did Engineer A demonstrate the professional virtues of prudence and integrity when choosing to rely on AI tools to compensate for acknowledged weaknesses?

Involves: Engineer A Integrity_AICase_Deception EngineerA_TechnicalWritingInsecurity

Analysis

Conclusion 7 Response

From a virtue ethics perspective (Q303), Engineer A's actions reveal a deficiency in the professional virtue of prudence—the practical wisdom to recognize one's limitations and act accordingly. While the desire to serve Client W demonstrates commitment, the decision to use unfamiliar AI tools to compensate for acknowledged weaknesses shows insufficient regard for the potential consequences to public welfare. A virtuous engineer would have prioritized competence development or sought appropriate collaboration rather than relying on technological substitutes for professional judgment and skill.

Cites: I.1. I.2.

Question 13 Counterfactual

II.1.c. III.9.

Would the ethical analysis change if Engineer A had disclosed the AI usage upfront and obtained explicit client consent before proceeding?

Involves: Engineer A Client W EngineerA_AI_Disclosure_ClientW Transparency_AICase_Attribution

Analysis

Analysis pending - insufficient information in case to address this question.

Question 14 Counterfactual

I.2. II.2.a.

What if Engineer A had declined the project due to the unavailability of Engineer B's mentorship and their own technical writing limitations?

Involves: Engineer A Engineer B EngineerA_MentorLoss_EngineerB_Retirement Technical_Writing_Competence

Analysis

Analysis pending - insufficient information in case to address this question.

Question 15 Counterfactual

I.1. II.1.c.

How would the ethical assessment differ if Engineer A had used proprietary AI tools with established professional validation rather than open-source software?

Involves: OpenSource_AI_ReportDrafting_Software ClientW_PublicDomainExposure_AIInterface AI_Tool_Verification_Constraint

Analysis

Analysis pending - insufficient information in case to address this question.

Question	Conclusion
Question 1 Board Question Was Engineer A’s use of AI to create the report text ethical, given that Engineer A thoroughly checked the report?	Conclusion 2 Board Conclusion Engineer A's use of AI in report writing was partly ethical, and partly unethical.
Question 2 Board Question 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?	Conclusion 1 Board Conclusion The use of AI-assisted drafting tools by Engineer A was not unethical per se.
Question 3 Board Question 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?	Conclusion 3 Board Conclusion 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). Conclusion 5 Analytical The Board's conclusion that disclosure is not required 'unless such disclosure is required under Engineer A's contract' establishes a contractual rather than ethical baseline for AI transparency. However, this approach may inadequately address the evolving nature of informed consent in professional services. When AI tools fundamentally alter the nature of professional work products—potentially affecting accuracy, originality, and liability—the traditional contract-based disclosure framework may need supplementation with professional judgment-based disclosure standards.
Question 4 Implicit Should Engineer A have sought alternative mentorship or quality assurance arrangements before proceeding with AI tools, given their acknowledged technical writing limitations?	Conclusion 6 Response Addressing the implicit question about alternative mentorship arrangements (Q101), Engineer A had several ethically preferable options before resorting to unfamiliar AI tools. These included: seeking temporary mentorship from other qualified professionals, requesting project timeline extensions to allow for skill development, or declining the project while referring Client W to more suitable practitioners. The choice to proceed with AI tools without adequate preparation represents a failure to exhaust reasonable alternatives for maintaining competence, particularly given the public safety implications of groundwater infrastructure design.
Question 5 Implicit What ethical obligations arise when an engineer uses unfamiliar technology tools in professional practice without adequate training or experience?	Conclusion 4 Analytical Beyond the Board's finding that Engineer A's AI use was 'partly ethical, and partly unethical,' the case reveals a critical gap in professional development frameworks for emerging technologies. Engineer A's situation—losing mentorship support precisely when facing unfamiliar AI tools—highlights how traditional competence maintenance structures may be inadequate for rapid technological change. The Board's conclusion implicitly recognizes that competence is not binary but contextual, requiring engineers to develop adaptive learning strategies rather than relying solely on established mentorship patterns.
Question 6 Implicit Does the open-source nature of the AI tools create additional ethical considerations regarding data security, intellectual property, and professional liability?	Conclusion 5 Analytical The Board's conclusion that disclosure is not required 'unless such disclosure is required under Engineer A's contract' establishes a contractual rather than ethical baseline for AI transparency. However, this approach may inadequately address the evolving nature of informed consent in professional services. When AI tools fundamentally alter the nature of professional work products—potentially affecting accuracy, originality, and liability—the traditional contract-based disclosure framework may need supplementation with professional judgment-based disclosure standards.
Question 7 Principle Tension How should Engineer A balance the Competence_AICase_Canon requirement against the Confidentiality_AICase_Upload obligation when AI tools require data input to function effectively?	Conclusion 8 Synthesis The case reveals an unresolved tension between the Competence_AICase_Canon and Confidentiality_AICase_Upload principles (Q201). Engineer A faced an impossible choice: either maintain client confidentiality by avoiding AI tools that require data upload, or pursue competence enhancement through AI assistance at the cost of potentially compromising confidential information. The Board's analysis suggests this tension was inadequately resolved, as Engineer A proceeded with data upload without explicit client consent, prioritizing perceived competence over confidentiality protection.
Question 8 Principle Tension Does the ResponsibleCharge_AICase_Violation principle conflict with the Transparency_AICase_Attribution principle when disclosure might undermine client confidence in professional judgment?	Conclusion 9 Synthesis The interaction between ResponsibleCharge_AICase_Violation and Transparency_AICase_Attribution principles demonstrates a fundamental challenge in AI-assisted professional practice. Engineer A's failure to adequately disclose AI contributions while maintaining responsible charge creates a paradox: claiming professional responsibility for work products while obscuring the actual sources of professional judgment. This case suggests that responsible charge in the AI era may require enhanced transparency standards to maintain the integrity of professional accountability.
Question 9 Principle Tension How does the ProfessionalJudgment_AICase_Substitute principle interact with the Competence_TechnicalWriting_Facts when an engineer's weakness in one area leads to AI dependency?	Insufficient information
Question 10 Theoretical From a deontological perspective, did Engineer A fulfill their categorical duty to maintain competence when using unfamiliar AI tools without adequate preparation?	Insufficient information
Question 11 Theoretical From a consequentialist perspective, do the potential benefits of AI-assisted engineering work justify the risks to public welfare and professional integrity identified in this case?	Insufficient information
Question 12 Theoretical From a virtue ethics perspective, did Engineer A demonstrate the professional virtues of prudence and integrity when choosing to rely on AI tools to compensate for acknowledged weaknesses?	Conclusion 7 Response From a virtue ethics perspective (Q303), Engineer A's actions reveal a deficiency in the professional virtue of prudence—the practical wisdom to recognize one's limitations and act accordingly. While the desire to serve Client W demonstrates commitment, the decision to use unfamiliar AI tools to compensate for acknowledged weaknesses shows insufficient regard for the potential consequences to public welfare. A virtuous engineer would have prioritized competence development or sought appropriate collaboration rather than relying on technological substitutes for professional judgment and skill.
Question 13 Counterfactual Would the ethical analysis change if Engineer A had disclosed the AI usage upfront and obtained explicit client consent before proceeding?	Insufficient information
Question 14 Counterfactual What if Engineer A had declined the project due to the unavailability of Engineer B's mentorship and their own technical writing limitations?	Insufficient information
Question 15 Counterfactual How would the ethical assessment differ if Engineer A had used proprietary AI tools with established professional validation rather than open-source software?	Insufficient information

Rich Analysis Results

View Extraction

Causal-Normative Links 6

Thorough Report Review

Fulfills

AI Output Verification Obligation
EngineerA_AI_Verification_Designs
EngineerA_Quality_Assurance

Violates None

Use AI Software Decision

Fulfills None

Violates

Tool Familiarity Obligation
EngineerA_Tool_Familiarity

Cursory Design Review

Fulfills None

Violates

AI Output Verification Obligation
Regulatory Compliance Verification Obligation
EngineerA_Quality_Assurance

Non-disclosure AI Usage

Fulfills None

Violates

AI Disclosure Obligation
EngineerA_AI_Disclosure_ClientW
EngineerA_Attribution_AI_Sources

Confidential Data Upload

Fulfills None

Violates

EngineerA_Confidentiality_ClientW

Professional Seal Application

Fulfills

EngineerA_Responsible_Charge

Violates

EngineerA_ResponsibleCharge_Design

Question Emergence 15

Triggering Events

Mentor Loss Event

Triggering Actions

Use AI Software Decision
Thorough Report Review
Non-disclosure_AI_Usage
Professional Seal Application

Competing Warrants

AI Output Verification Obligation AI Disclosure Obligation
Tool Familiarity Obligation Supervision Transition Obligation

Triggering Events

Mentor Loss Event
Use AI Software Decision

Triggering Actions

Cursory Design Review
Non-disclosure_AI_Usage
Professional Seal Application

Competing Warrants

AI Output Verification Obligation Tool Familiarity Obligation
AI Disclosure Obligation Supervision Transition Obligation

Triggering Events

Use AI Software Decision
Non-disclosure_AI_Usage

Triggering Actions

Use AI Software Decision
Non-disclosure_AI_Usage
Professional Seal Application

Competing Warrants

AI Disclosure Obligation EngineerA_Confidentiality_ClientW
EngineerA_Attribution_AI_Sources EngineerA_ResponsibleCharge_Design

Triggering Events

Mentor Loss Event

Triggering Actions

Use AI Software Decision

Competing Warrants

Supervision Transition Obligation Tool Familiarity Obligation

Triggering Events

Mentor Loss Event

Triggering Actions

Use AI Software Decision
Professional Seal Application

Competing Warrants

Tool Familiarity Obligation Supervision Transition Obligation
AI Output Verification Obligation Regulatory Compliance Verification Obligation

Triggering Events

Use AI Software Decision
Confidential Data Upload

Triggering Actions

Use AI Software Decision
Confidential Data Upload

Competing Warrants

EngineerA_Confidentiality_ClientW Tool Familiarity Obligation
EngineerA_Responsible_Charge EngineerA_Competence_AI_Tools

Triggering Events

Mentor Loss Event

Triggering Actions

Use AI Software Decision
Confidential Data Upload

Competing Warrants

EngineerA_Competence_AI_Tools EngineerA_Confidentiality_ClientW
Tool Familiarity Obligation Supervision Transition Obligation

Triggering Events

Use AI Software Decision
Non-disclosure_AI_Usage
Professional Seal Application

Triggering Actions

Non-disclosure_AI_Usage
Professional Seal Application

Competing Warrants

AI Disclosure Obligation EngineerA_Responsible_Charge
EngineerA_AI_Disclosure_ClientW EngineerA_ResponsibleCharge_Design

Triggering Events

Mentor Loss Event

Triggering Actions

Use AI Software Decision
Non-disclosure_AI_Usage

Competing Warrants

Tool Familiarity Obligation Supervision Transition Obligation
AI Disclosure Obligation EngineerA_Competence_AI_Tools

Triggering Events

Mentor Loss Event

Triggering Actions

Use AI Software Decision
Cursory Design Review

Competing Warrants

Tool Familiarity Obligation Supervision Transition Obligation
EngineerA_Tool_Familiarity EngineerA_Competence_AI_Tools

Triggering Events

Mentor Loss Event
Quality Discrepancy Discovery
Technical Error Detection

Triggering Actions

Use AI Software Decision
Cursory Design Review
Non-disclosure_AI_Usage
Confidential Data Upload
Professional Seal Application

Competing Warrants

Tool Familiarity Obligation Supervision Transition Obligation
AI Output Verification Obligation Regulatory Compliance Verification Obligation
AI Disclosure Obligation EngineerA_Safety_PublicWelfare

Triggering Events

Mentor Loss Event
Quality Discrepancy Discovery
Technical Error Detection

Triggering Actions

Use AI Software Decision
Non-disclosure_AI_Usage
Confidential Data Upload
Professional Seal Application

Competing Warrants

Supervision Transition Obligation Tool Familiarity Obligation
AI Output Verification Obligation AI Disclosure Obligation

Triggering Events

Mentor Loss Event

Triggering Actions

Use AI Software Decision
Non-disclosure_AI_Usage
Professional Seal Application

Competing Warrants

AI Disclosure Obligation Tool Familiarity Obligation
AI Output Verification Obligation Supervision Transition Obligation

Triggering Events

Mentor Loss Event

Triggering Actions

None

Competing Warrants

Supervision Transition Obligation EngineerA_Competence_AI_Tools
EngineerA_Quality_Assurance EngineerA_Responsible_Charge

Triggering Events

Use AI Software Decision
Technical Error Detection
Quality Discrepancy Discovery

Triggering Actions

Non-disclosure_AI_Usage
Cursory Design Review
Professional Seal Application

Competing Warrants

Tool Familiarity Obligation AI Output Verification Obligation
Regulatory Compliance Verification Obligation AI Disclosure Obligation

Resolution Patterns 9

Determinative Principles

Competence requirement
Professional responsibility for work products
Adequate review and verification

Determinative Facts

Engineer A thoroughly checked the report
Engineer A had acknowledged technical writing limitations
AI was used for report text generation

Determinative Principles

Tool neutrality principle
Professional judgment primacy
Adequate supervision and review

Determinative Facts

Engineer A reviewed the design at a high level
AI tools are similar to other software used in design
Engineer maintained professional oversight

Determinative Principles

Tool transparency standards
Contractual obligations primacy
Professional work product ownership

Determinative Facts

AI tools are analogous to other engineering software
No contractual disclosure requirement existed
Engineer maintained professional responsibility for outputs

Determinative Principles

Competence maintenance
Adaptive learning requirements
Professional development adequacy

Determinative Facts

Engineer A lost mentorship support
Unfamiliar AI tools were used
Traditional competence structures proved inadequate

Determinative Principles

Informed consent evolution
Professional service transparency
Contractual vs. ethical obligations

Determinative Facts

AI tools fundamentally alter work products
Traditional contract frameworks may be inadequate
Professional judgment-based standards may be needed

Determinative Principles

Alternative competence maintenance
Public safety prioritization
Professional limitation recognition

Determinative Facts

Public safety implications of groundwater infrastructure
Available alternative approaches
Failure to exhaust reasonable alternatives

Determinative Principles

Professional virtue of prudence
Practical wisdom
Limitation recognition

Determinative Facts

Acknowledged weaknesses
Insufficient regard for consequences
Technological substitution for professional judgment

Determinative Principles

Competence vs. confidentiality tension
Client consent requirements
Data protection obligations

Determinative Facts

AI tools required data upload
No explicit client consent obtained
Impossible choice between competing obligations

Determinative Principles

Responsible charge integrity
Professional accountability transparency
AI-era responsibility standards

Determinative Facts

Failure to adequately disclose AI contributions
Maintained responsible charge claims
Obscured sources of professional judgment

Loading entity-grounded arguments...

Decision Points

View Extraction

Legend: PRO CON | N% = Validation Score

DP1 Engineer A must decide how to handle confidential client data when using AI tools

Should Engineer A obtain client consent before uploading confidential project data to open-source AI systems?

Options:

Obtain client consent before uploading data to AI systems
Upload confidential data to open-source AI without client consent

Arguments:

A1 Score: 40%

Engineer A should obtain client consent before uploading data to AI systems

Because Client Confidentiality Obligation requires this action

A2 Score: 60%

Engineer A should NOT obtain client consent before uploading data to AI systems

Because AI Disclosure Obligation would be violated

A3 Score: 40%

Engineer A should upload confidential data to open-source AI without client consent

Because this promotes Open Access

A4 Score: 60%

Engineer A should NOT upload confidential data to open-source AI without client consent

Because AI Disclosure Obligation would be violated

85% aligned

DP2 Engineer A must decide whether to cite AI tools and technical sources in deliverables

Should Engineer A provide proper attribution for AI-generated content and cite technical sources used in the report?

Options:

Cite AI tools and technical sources in deliverables
Omit attribution for AI-generated content

Arguments:

A5 Score: 40%

Engineer A should adopt the Cite AI tools and technical sources in deliverables

Because Attribution Obligation requires this action

A6 Score: 60%

Engineer A should NOT adopt the Cite AI tools and technical sources in deliverables

Because this may reduce necessary human judgment and oversight

A7 Score: 40%

Engineer A should omit attribution for AI-generated content

Because this promotes Professional Judgment

A8 Score: 60%

Engineer A should NOT omit attribution for AI-generated content

Because this may reduce necessary human judgment and oversight

80% aligned

DP3 Engineer A must decide the level of review to apply to AI-generated design documents

Should Engineer A conduct comprehensive review of AI-generated design documents or rely on high-level review only?

Options:

Conduct comprehensive review of AI-generated designs before sealing
Conduct only high-level review of AI-generated designs

Arguments:

A9 Score: 60%

Engineer A should conduct comprehensive review of AI-generated designs before sealing

Because Responsible Charge Obligation requires this action

A10 Score: 60%

Engineer A should NOT conduct comprehensive review of AI-generated designs before sealing

Because this may reduce necessary human judgment and oversight

A11 Score: 60%

Engineer A should conduct only high-level review of AI-generated designs

Because this promotes Efficiency

A12 Score: 60%

Engineer A should NOT conduct only high-level review of AI-generated designs

Because this may reduce necessary human judgment and oversight

90% aligned

DP4 Engineer A must decide whether to disclose AI tool usage to Client W

Should Engineer A disclose the use of AI tools in generating the engineering deliverables to Client W?

Options:

Disclose AI tool usage to client
Do not disclose AI tool usage to client

Arguments:

A13 Score: 60%

Engineer A should disclose AI tool usage to client

Because this promotes Disclosure

A14 Score: 60%

Engineer A should NOT disclose AI tool usage to client

Because EngineerA_Confidentiality_ClientW would be violated

A15 Score: 60%

Engineer A should not disclose AI tool usage to client

Because this promotes Disclosure

A16 Score: 60%

Engineer A should disclose AI tool usage to client

Because EngineerA_Confidentiality_ClientW would be violated

85% aligned

Case Narrative

Phase 4 narrative construction results for Case 7

Characters

Events

Conflicts

Fluents

Opening Context

You are a supervising engineer facing a critical juncture in your career: your longtime mentor has recently departed the company, leaving you to navigate uncharted territory as your team begins incorporating AI tools into project workflows for the first time. As draft submissions pile up on your desk, you must establish new protocols for proper attribution and oversight of AI-assisted work while ensuring the integrity of your engineering reports and designs. The decisions you make now will set precedents that could impact both your team's professional standards and the broader engineering community's approach to emerging technologies.

From the perspective of Engineer A

Characters (3)

Engineer A Protagonist

A supervising engineer responsible for overseeing project transitions and ensuring proper attribution of AI-assisted work in reports and designs.

Motivations:

Seeks to maintain professional standards and regulatory compliance while navigating the emerging challenges of AI integration in engineering practice.

Client W Stakeholder

A project stakeholder who has contracted engineering services and expects deliverables that meet regulatory standards and professional obligations.

Motivations:

Desires timely, compliant, and high-quality engineering work that fulfills contractual requirements and regulatory expectations.

Engineer B Stakeholder

A practicing engineer involved in a supervision transition who must demonstrate competency with required tools and maintain regulatory compliance.

Motivations:

Aims to successfully complete the professional transition while ensuring technical proficiency and adherence to engineering standards.

Ethical Tensions (5)

Engineer A is obligated to verify AI outputs but lacks sufficient experience with AI tools to perform adequate verification, creating a competence gap that could compromise public safety LLM

AI Output Verification Obligation AI_Tool_Experience_Limitation

Obligation vs Constraint

Affects: Engineer A Environmental Engineer Role Client W

Moral Intensity (Jones 1991):

Magnitude: high Probability: high immediate direct concentrated

Engineer A must maintain responsible charge over the work while being limited to only verification of AI-generated designs, potentially creating a gap between accountability and actual control over the engineering work LLM

EngineerA_Responsible_Charge EngineerA_Technology_Verification

Obligation vs Obligation

Affects: Engineer A Environmental Engineer Role Client W

Moral Intensity (Jones 1991):

Magnitude: high Probability: medium immediate direct concentrated

Engineer A needs proper supervision during transition to using AI tools but mentorship is unavailable, forcing independent decision-making in unfamiliar territory with potential safety implications LLM

Supervision Transition Obligation Mentor_Unavailability

Obligation vs Constraint

Affects: Engineer A Engineering Mentor Role Environmental Engineer Role

Moral Intensity (Jones 1991):

Magnitude: medium Probability: high immediate direct concentrated

Engineer A must disclose AI tool usage while maintaining client confidentiality, creating potential conflicts about what information can be shared publicly about AI-assisted work on confidential projects LLM

AI Disclosure Obligation Client_W_Confidentiality_Boundary

Obligation vs Constraint

Affects: Engineer A Client W Board of Ethical Review (BER)

Moral Intensity (Jones 1991):

Magnitude: medium Probability: medium near-term direct concentrated

Engineer A must ensure quality assurance while preserving independent engineering judgment, but over-reliance on AI tools may compromise the ability to exercise proper professional judgment in quality assessment LLM

EngineerA_Quality_Assurance Engineering_Judgment_Preservation

Obligation vs Constraint

Affects: Engineer A Environmental Engineer Role Client W

Moral Intensity (Jones 1991):

Magnitude: high Probability: medium immediate direct concentrated

States (10)

Mentor Loss State AI Tool First-Use State Draft Submission State EngineerA_MentorLoss_EngineerB_Retirement EngineerA_AIFirstUse_OpenSourceAI EngineerA_TechnicalWritingInsecurity EngineerA_DraftSubmission_ClientWReport EngineerA_InsufficientAttribution_AIContent EngineerA_NonCompliant_DesignDocuments Compromised Oversight State

Event Timeline (13)

#	Event	Type
1	A junior engineer faces their first major project deadline while dealing with the recent departure of their experienced mentor, creating pressure to rely on unfamiliar AI tools for design assistance. This situation establishes the foundational challenge of maintaining professional standards while navigating new technology without proper guidance.	state
2	The engineer decides to use AI software to help complete the engineering design work, marking their first significant reliance on artificial intelligence for professional calculations and recommendations. This decision represents a critical turning point that will influence all subsequent actions in the case.	action
3	The engineer conducts a comprehensive review of the AI-generated report, carefully examining the calculations, assumptions, and recommendations for accuracy and completeness. This thorough analysis demonstrates proper due diligence in validating AI-assisted work before proceeding.	action
4	Under time pressure, the engineer performs only a superficial review of the underlying design elements, failing to conduct the detailed verification that professional standards require. This cursory examination creates potential risks that may not be immediately apparent.	action
5	The engineer chooses not to disclose to the client or project stakeholders that AI software was used in developing the engineering analysis and recommendations. This omission raises questions about transparency and informed consent in professional practice.	action
6	Proprietary client data and sensitive project information are uploaded to the AI platform without explicit permission or consideration of data security protocols. This action potentially violates confidentiality obligations and exposes sensitive information to unauthorized systems.	action
7	The engineer applies their professional seal to the final deliverable, taking legal and ethical responsibility for all work contained within the document. This action certifies that the work meets professional standards and has been performed under the engineer's direct supervision.	action
8	The departure of the engineer's experienced mentor removes crucial guidance and oversight at a critical moment in the project timeline. This loss of mentorship eliminates an important safeguard for ensuring proper professional practices and decision-making.	automatic
9	Quality Discrepancy Discovery	automatic
10	Technical Error Detection	automatic
11	Engineer A is obligated to verify AI outputs but lacks sufficient experience with AI tools to perform adequate verification, creating a competence gap that could compromise public safety	automatic
12	Engineer A must maintain responsible charge over the work while being limited to only verification of AI-generated designs, potentially creating a gap between accountability and actual control over the engineering work	automatic
13	Engineer A's use of AI in report writing was partly ethical, and partly unethical.	outcome

Timeline Flow

Sequential action-event relationships. See Analysis tab for action-obligation links.

Enables (action → event)

Use AI Software Decision Thorough Report Review
Thorough Report Review Cursory Design Review
Cursory Design Review Non-disclosure_AI_Usage
Non-disclosure_AI_Usage Confidential Data Upload
Confidential Data Upload Professional Seal Application
Professional Seal Application Mentor Loss Event

Key Takeaways

Engineers must possess adequate competence to verify AI-generated outputs before taking professional responsibility for them, as verification without understanding compromises public safety.
The principle of responsible charge requires engineers to maintain meaningful control over engineering work, which becomes challenging when AI tools generate designs that exceed the engineer's expertise to properly evaluate.
Professional development and mentorship become critical ethical obligations when new technologies like AI create competence gaps that could affect engineering judgment and public welfare.

Step 4: Synthesis Review

161

9

15

9

Stalemate

Full Entity Graph

Entity Types

Synthesis Reasoning Flow

Node Types & Relationships

NSPE Code Provisions Referenced

I.1. I.1.

I.2. I.2.

I.5. I.5.

II.1.c. II.1.c.

II.2.a. II.2.a.

II.2.b. II.2.b.

III.3. III.3.

III.8.a. III.8.a.

III.9. III.9.

Questions & Conclusions

Question 1 Board Question

Question 2 Board Question

Question 3 Board Question

Question 4 Implicit

Question 5 Implicit

Question 6 Implicit

Question 7 Principle Tension

Question 8 Principle Tension

Question 9 Principle Tension

Question 10 Theoretical

Question 11 Theoretical

Question 12 Theoretical

Question 13 Counterfactual

Question 14 Counterfactual

Question 15 Counterfactual

Rich Analysis Results

Causal-Normative Links 6

Thorough Report Review

Use AI Software Decision

Cursory Design Review

Non-disclosure AI Usage

Confidential Data Upload

Professional Seal Application

Question Emergence 15

Was Engineer A's use of AI to create the report text ethical, given that Engineer A thoroughly checked the report? 5 triggers 2 competing warrants

Triggering Events

Triggering Actions

Competing Warrants

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? 5 triggers 2 competing warrants

Triggering Events

Triggering Actions

Competing Warrants

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? 5 triggers 2 competing warrants

Triggering Events

Triggering Actions

Competing Warrants

Should Engineer A have sought alternative mentorship or quality assurance arrangements before proceeding with AI tools, given their acknowledged technical writing limitations? 2 triggers 1 competing warrants

Triggering Events

Triggering Actions

Competing Warrants

What ethical obligations arise when an engineer uses unfamiliar technology tools in professional practice without adequate training or experience? 3 triggers 2 competing warrants

Triggering Events

Triggering Actions

Competing Warrants

Does the open-source nature of the AI tools create additional ethical considerations regarding data security, intellectual property, and professional liability? 4 triggers 2 competing warrants

Triggering Events

Triggering Actions

Competing Warrants

How should Engineer A balance the Competence_AICase_Canon requirement against the Confidentiality_AICase_Upload obligation when AI tools require data input to function effectively? 3 triggers 2 competing warrants

Triggering Events

Triggering Actions

Competing Warrants

Does the ResponsibleCharge_AICase_Violation principle conflict with the Transparency_AICase_Attribution principle when disclosure might undermine client confidence in professional judgment? 5 triggers 2 competing warrants

Triggering Events

Triggering Actions

Competing Warrants

How does the ProfessionalJudgment_AICase_Substitute principle interact with the Competence_TechnicalWriting_Facts when an engineer's weakness in one area leads to AI dependency? 3 triggers 2 competing warrants

Triggering Events

Triggering Actions