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32 entities 7 actions 6 events 8 causal chains 10 temporal relations
Timeline Overview
Action Event 13 sequenced markers
Cursory Design Review During design document preparation, before submission to Client W
Mentor Retirement Before Engineer A was retained by Client W
AI Tool Adoption At project outset, after Engineer B's retirement
Confidential Data Input During report and design document drafting
Thorough Report Review After receiving the AI-generated first draft of the report
Report Sealing and Submission At report submission to Client W
Design AI Disclosure Omission At design document submission
Revision Instruction After Client W's review of the deliverables
AI Report Generation After Engineer A input Client W's information into the AI software
Confidential Information Exposure At the moment Engineer A input Client W's information into the AI software
AI Design Generation After Engineer A entered Client W's information into the AI drafting software
Report Inconsistency Observation When Client W reviewed the draft report
Design Error Discovery When Client W reviewed the design documents
OWL-Time Temporal Structure 10 relations time: = w3.org/2006/time
Engineer B providing mentorship and quality assurance reviews time:intervalBefore Engineer B's retirement
Engineer B's retirement time:intervalBefore Engineer A retained by Client W
Engineer A observing the groundwater monitoring site time:intervalOverlaps Engineer A retained by Client W to prepare the report and design documents
Engineer A inputting Client W's information into the AI software time:intervalBefore receiving the first draft of the report
AI generation of the report draft time:intervalBefore Engineer A's thorough review and cross-checking of the report
Engineer A's thorough review and personalization of the report time:intervalBefore submission of the sealed draft report to Client W
AI-assisted generation of preliminary design documents time:intervalBefore Engineer A's cursory review and site-specific adjustments
submission of the draft report and design documents time:intervalBefore Client W's review
Client W's identification of errors in the design documents time:intervalBefore Client W's instruction to Engineer A to revise the plans
the case events (report and design document preparation) time:intervalBefore the Discussion section's retrospective ethical analysis
Extracted Actions (7)
Volitional professional decisions with intentions and ethical context

Description: Facing delivery of both a report and design documents without Engineer B's review and mentorship, Engineer A chose to use open-sourced AI software new to the market, with which Engineer A had no prior experience, for drafting.

Temporal Marker: At project outset, after Engineer B's retirement instant

Fluent Transitions:
Initiates (1)
  • AI Reliance Without Human QA Support

Mental State: deliberate

Intended Outcome: Compensate for lost mentorship and lack of confidence in technical writing while delivering both work products to Client W

Foreseen Unintended Effects:

  • Unfamiliarity with the tool's accuracy and originality of generated content
Guided By Principles:
  • Perform Services Only in Areas of Competence
Required Capabilities:
Familiarity with AI tool functionality Professional judgment in tool selection
Within Competence: Yes
Field classification (triples vs literals)
Relations (structural triples)
  • guidedByPrinciple: Perform Services Only in Areas of Competence
  • requiresCapability: Familiarity with AI tool functionality; Professional judgment in tool selection
  • initiates: AI Reliance Without Human QA Support
Literal extractions (kept for synthesis)
  • description content: Facing delivery of both a report and design documents without Engineer B's review and mentorship, Engineer A chose to use open-sourced AI software new to the market, with which Engineer A had no prior experience, for drafting.
  • hasAgent content: Engineer A
  • temporalMarker content: At project outset, after Engineer B's retirement
  • eventRoleContext content: Licensed Environmental Engineer
  • hasMentalState content: deliberate
  • intendedOutcome content: Compensate for lost mentorship and lack of confidence in technical writing while delivering both work products to Client W
  • foreseenUnintendedEffects content: Unfamiliarity with the tool's accuracy and originality of generated content
  • textReferences content: Engineer A opted to use open-sourced artificial intelligence (AI) software to create an initial draft of the necessary report and to use AI-assisted drafting tools to generate preliminary design documents; The AI drafting software was new to the market and Engineer A had no previous experience with the tool.
  • temporalExtent content: instant
  • temporalSequence content: 2
  • withinCompetence assessment: True
RDF JSON-LD
{
  "@context": {
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    "proeth-case": "http://proethica.org/ontology/case/7#",
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    "rdfs": "http://www.w3.org/2000/01/rdf-schema#",
    "time": "http://www.w3.org/2006/time#"
  },
  "@id": "http://proethica.org/ontology/case/7#Action_AI_Tool_Adoption",
  "@type": "proeth:Action",
  "proeth:description": "Facing delivery of both a report and design documents without Engineer B\u0027s review and mentorship, Engineer A chose to use open-sourced AI software new to the market, with which Engineer A had no prior experience, for drafting.",
  "proeth:eventRoleContext": "Licensed Environmental Engineer",
  "proeth:foreseenUnintendedEffects": [
    "Unfamiliarity with the tool\u0027s accuracy and originality of generated content"
  ],
  "proeth:guidedByPrinciple": [
    "Perform Services Only in Areas of Competence"
  ],
  "proeth:hasAgent": "Engineer A",
  "proeth:hasMentalState": "deliberate",
  "proeth:initiates": [
    "AI Reliance Without Human QA Support"
  ],
  "proeth:intendedOutcome": "Compensate for lost mentorship and lack of confidence in technical writing while delivering both work products to Client W",
  "proeth:requiresCapability": [
    "Familiarity with AI tool functionality",
    "Professional judgment in tool selection"
  ],
  "proeth:temporalExtent": "instant",
  "proeth:temporalMarker": "At project outset, after Engineer B\u0027s retirement",
  "proeth:temporalSequence": 2,
  "proeth:textReferences": [
    "Engineer A opted to use open-sourced artificial intelligence (AI) software to create an initial draft of the necessary report and to use AI-assisted drafting tools to generate preliminary design documents",
    "The AI drafting software was new to the market and Engineer A had no previous experience with the tool."
  ],
  "proeth:withinCompetence": true,
  "rdfs:label": "AI Tool Adoption"
}

Description: Engineer A entered information provided by Client W into the open-source AI interface to generate the report draft and preliminary designs, without obtaining Client W's consent, which the Board characterized as placing the client's private information in the public domain.

Temporal Marker: During report and design document drafting instant

Fluent Transitions:
Initiates (1)
  • Client Confidentiality Breach

Mental State: deliberate

Intended Outcome: Enable the AI software to synthesize the client's information into a draft report and preliminary design documents

Foreseen Unintended Effects:

  • Exposure of client's private information through an open-source platform
Obligation Engagement:
Violates (1)
  • Client Confidentiality (Code II.1.c)
Required Capabilities:
Understanding of data privacy implications of open-source AI tools
Within Competence: Yes
Field classification (triples vs literals)
Relations (structural triples)
  • violatesObligation: Client Confidentiality (Code II.1.c)
  • requiresCapability: Understanding of data privacy implications of open-source AI tools
  • initiates: Client Confidentiality Breach
Literal extractions (kept for synthesis)
  • description content: Engineer A entered information provided by Client W into the open-source AI interface to generate the report draft and preliminary designs, without obtaining Client W's consent, which the Board characterized as placing the client's private information in the public domain.
  • hasAgent content: Engineer A
  • temporalMarker content: During report and design document drafting
  • eventRoleContext content: Licensed Environmental Engineer
  • hasMentalState content: deliberate
  • intendedOutcome content: Enable the AI software to synthesize the client's information into a draft report and preliminary design documents
  • foreseenUnintendedEffects content: Exposure of client's private information through an open-source platform
  • textReferences content: Engineer A input the information gathered from Client W into the AI software, and, after a few refining prompts, received a first draft of the report generated by the AI software.; When Engineer A uploaded Client W’s information into the AI open-source interface, this was tantamount to placing the Client’s private information in the public domain.
  • temporalExtent content: instant
  • temporalSequence content: 3
  • withinCompetence assessment: True
RDF JSON-LD
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  "@context": {
    "proeth": "http://proethica.org/ontology/intermediate#",
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  },
  "@id": "http://proethica.org/ontology/case/7#Action_Confidential_Data_Input",
  "@type": "proeth:Action",
  "proeth:description": "Engineer A entered information provided by Client W into the open-source AI interface to generate the report draft and preliminary designs, without obtaining Client W\u0027s consent, which the Board characterized as placing the client\u0027s private information in the public domain.",
  "proeth:eventRoleContext": "Licensed Environmental Engineer",
  "proeth:foreseenUnintendedEffects": [
    "Exposure of client\u0027s private information through an open-source platform"
  ],
  "proeth:fulfillsObligation": [],
  "proeth:hasAgent": "Engineer A",
  "proeth:hasMentalState": "deliberate",
  "proeth:initiates": [
    "Client Confidentiality Breach"
  ],
  "proeth:intendedOutcome": "Enable the AI software to synthesize the client\u0027s information into a draft report and preliminary design documents",
  "proeth:raisesObligation": [],
  "proeth:requiresCapability": [
    "Understanding of data privacy implications of open-source AI tools"
  ],
  "proeth:temporalExtent": "instant",
  "proeth:temporalMarker": "During report and design document drafting",
  "proeth:temporalSequence": 3,
  "proeth:textReferences": [
    "Engineer A input the information gathered from Client W into the AI software, and, after a few refining prompts, received a first draft of the report generated by the AI software.",
    "When Engineer A uploaded Client W\u2019s information into the AI open-source interface, this was tantamount to placing the Client\u2019s private information in the public domain."
  ],
  "proeth:violatesObligation": [
    "Client Confidentiality (Code II.1.c)"
  ],
  "proeth:withinCompetence": true,
  "rdfs:label": "Confidential Data Input"
}

Description: Engineer A conducted a thorough review of the AI-generated report, cross-checking key facts against professional journal articles, running search engine queries to verify originality, and personalizing the wording, keeping the document under Engineer A's direction and control.

Temporal Marker: After receiving the AI-generated first draft of the report interval

Fluent Transitions:
Initiates (1)
  • Report Under Direction and Control

Mental State: deliberate

Intended Outcome: Ensure the accuracy and originality of the AI-generated report content before submission

Obligation Engagement:
Fulfills (1)
  • Direction and Control (Code II.2.b)
Guided By Principles:
  • Avoid Deceptive Acts
  • Perform Services Only in Areas of Competence
Required Capabilities:
Environmental engineering expertise Groundwater data analysis Technical fact verification
Within Competence: Yes
Field classification (triples vs literals)
Relations (structural triples)
  • fulfillsObligation: Direction and Control (Code II.2.b)
  • guidedByPrinciple: Avoid Deceptive Acts; Perform Services Only in Areas of Competence
  • requiresCapability: Environmental engineering expertise; Groundwater data analysis; Technical fact verification
  • initiates: Report Under Direction and Control
Literal extractions (kept for synthesis)
  • description content: Engineer A conducted a thorough review of the AI-generated report, cross-checking key facts against professional journal articles, running search engine queries to verify originality, and personalizing the wording, keeping the document under Engineer A's direction and control.
  • hasAgent content: Engineer A
  • temporalMarker content: After receiving the AI-generated first draft of the report
  • eventRoleContext content: Licensed Environmental Engineer
  • hasMentalState content: deliberate
  • intendedOutcome content: Ensure the accuracy and originality of the AI-generated report content before submission
  • textReferences content: Engineer A conducted a thorough review of the report, cross-checking key facts against professional journal articles and verifying the phrasing by running search engine queries to ensure the content did not match any existing language; Engineer A also made minor adjustments to some of the wording to personalize the content.
  • temporalExtent content: interval
  • temporalSequence content: 7
  • withinCompetence assessment: True
RDF JSON-LD
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    "proeth": "http://proethica.org/ontology/intermediate#",
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  },
  "@id": "http://proethica.org/ontology/case/7#Action_Thorough_Report_Review",
  "@type": "proeth:Action",
  "proeth:description": "Engineer A conducted a thorough review of the AI-generated report, cross-checking key facts against professional journal articles, running search engine queries to verify originality, and personalizing the wording, keeping the document under Engineer A\u0027s direction and control.",
  "proeth:eventRoleContext": "Licensed Environmental Engineer",
  "proeth:fulfillsObligation": [
    "Direction and Control (Code II.2.b)"
  ],
  "proeth:guidedByPrinciple": [
    "Avoid Deceptive Acts",
    "Perform Services Only in Areas of Competence"
  ],
  "proeth:hasAgent": "Engineer A",
  "proeth:hasMentalState": "deliberate",
  "proeth:initiates": [
    "Report Under Direction and Control"
  ],
  "proeth:intendedOutcome": "Ensure the accuracy and originality of the AI-generated report content before submission",
  "proeth:raisesObligation": [],
  "proeth:requiresCapability": [
    "Environmental engineering expertise",
    "Groundwater data analysis",
    "Technical fact verification"
  ],
  "proeth:temporalExtent": "interval",
  "proeth:temporalMarker": "After receiving the AI-generated first draft of the report",
  "proeth:temporalSequence": 7,
  "proeth:textReferences": [
    "Engineer A conducted a thorough review of the report, cross-checking key facts against professional journal articles and verifying the phrasing by running search engine queries to ensure the content did not match any existing language",
    "Engineer A also made minor adjustments to some of the wording to personalize the content."
  ],
  "proeth:violatesObligation": [],
  "proeth:withinCompetence": true,
  "rdfs:label": "Thorough Report Review"
}

Description: Engineer A completed only a cursory review of the AI-generated design plans, adjusting certain elements for site-specific conditions, and failed to detect misaligned dimensions and omitted safety features required by local regulations.

Temporal Marker: During design document preparation, before submission to Client W interval

Fluent Transitions:
Initiates (2)
  • Design Deficiency and Public Safety Risk
  • Responsible Charge Lapse

Mental State: deliberate

Intended Outcome: Finalize the preliminary design documents efficiently by relying on the AI-assisted drafting output

Foreseen Unintended Effects:

  • Undetected errors in AI-generated plans
  • Potential regulatory noncompliance
Obligation Engagement:
Violates (2)
  • Direction and Control (Code II.2.b)
  • Responsible Charge (Code III.8.a)
Guided By Principles:
  • Hold Paramount Public Safety, Health, and Welfare
Required Capabilities:
Design verification and quality assurance review Knowledge of local regulatory requirements
Within Competence: Yes
Field classification (triples vs literals)
Relations (structural triples)
  • violatesObligation: Direction and Control (Code II.2.b); Responsible Charge (Code III.8.a)
  • guidedByPrinciple: Hold Paramount Public Safety, Health, and Welfare
  • requiresCapability: Design verification and quality assurance review; Knowledge of local regulatory requirements
  • initiates: Design Deficiency and Public Safety Risk; Responsible Charge Lapse
Literal extractions (kept for synthesis)
  • description content: Engineer A completed only a cursory review of the AI-generated design plans, adjusting certain elements for site-specific conditions, and failed to detect misaligned dimensions and omitted safety features required by local regulations.
  • hasAgent content: Engineer A
  • temporalMarker content: During design document preparation, before submission to Client W
  • eventRoleContext content: Licensed Environmental Engineer
  • hasMentalState content: deliberate
  • intendedOutcome content: Finalize the preliminary design documents efficiently by relying on the AI-assisted drafting output
  • foreseenUnintendedEffects content: Undetected errors in AI-generated plans; Potential regulatory noncompliance
  • textReferences content: Engineer A completed a cursory review of the AI-generated plans and adjusted certain elements to align with site-specific conditions.; the identified errors suggest insufficient review, which could compromise public welfare and impinge on Engineer A’s ethical and professional obligations
  • temporalExtent content: interval
  • temporalSequence content: 8
  • withinCompetence assessment: True
RDF JSON-LD
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  "@context": {
    "proeth": "http://proethica.org/ontology/intermediate#",
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    "time": "http://www.w3.org/2006/time#"
  },
  "@id": "http://proethica.org/ontology/case/7#Action_Cursory_Design_Review",
  "@type": "proeth:Action",
  "proeth:description": "Engineer A completed only a cursory review of the AI-generated design plans, adjusting certain elements for site-specific conditions, and failed to detect misaligned dimensions and omitted safety features required by local regulations.",
  "proeth:eventRoleContext": "Licensed Environmental Engineer",
  "proeth:foreseenUnintendedEffects": [
    "Undetected errors in AI-generated plans",
    "Potential regulatory noncompliance"
  ],
  "proeth:fulfillsObligation": [],
  "proeth:guidedByPrinciple": [
    "Hold Paramount Public Safety, Health, and Welfare"
  ],
  "proeth:hasAgent": "Engineer A",
  "proeth:hasMentalState": "deliberate",
  "proeth:initiates": [
    "Design Deficiency and Public Safety Risk",
    "Responsible Charge Lapse"
  ],
  "proeth:intendedOutcome": "Finalize the preliminary design documents efficiently by relying on the AI-assisted drafting output",
  "proeth:raisesObligation": [],
  "proeth:requiresCapability": [
    "Design verification and quality assurance review",
    "Knowledge of local regulatory requirements"
  ],
  "proeth:temporalExtent": "interval",
  "proeth:temporalMarker": "During design document preparation, before submission to Client W",
  "proeth:temporalSequence": 8,
  "proeth:textReferences": [
    "Engineer A completed a cursory review of the AI-generated plans and adjusted certain elements to align with site-specific conditions.",
    "the identified errors suggest insufficient review, which could compromise public welfare and impinge on Engineer A\u2019s ethical and professional obligations"
  ],
  "proeth:violatesObligation": [
    "Direction and Control (Code II.2.b)",
    "Responsible Charge (Code III.8.a)"
  ],
  "proeth:withinCompetence": true,
  "rdfs:label": "Cursory Design Review"
}

Description: Engineer A submitted the draft report to Client W for review, marking it clearly as a draft and applying their seal consistent with state law, but chose not to cite the use of AI software or its large language models.

Temporal Marker: At report submission to Client W instant

Fluent Transitions:
Initiates (1)
  • Undisclosed AI Contribution to Report

Mental State: deliberate

Intended Outcome: Deliver a polished, verified draft report to Client W for review

Foreseen Unintended Effects:

  • Client unaware of the extent of AI contribution to the report
Obligation Engagement:
Violates (1)
  • Give Credit for Engineering Work (Code III.9)
Required Capabilities:
Authority to seal engineering documents Environmental engineering expertise
Within Competence: Yes
Field classification (triples vs literals)
Relations (structural triples)
  • violatesObligation: Give Credit for Engineering Work (Code III.9)
  • requiresCapability: Authority to seal engineering documents; Environmental engineering expertise
  • initiates: Undisclosed AI Contribution to Report
Literal extractions (kept for synthesis)
  • description content: Engineer A submitted the draft report to Client W for review, marking it clearly as a draft and applying their seal consistent with state law, but chose not to cite the use of AI software or its large language models.
  • hasAgent content: Engineer A
  • temporalMarker content: At report submission to Client W
  • eventRoleContext content: Licensed Environmental Engineer
  • hasMentalState content: deliberate
  • intendedOutcome content: Deliver a polished, verified draft report to Client W for review
  • foreseenUnintendedEffects content: Client unaware of the extent of AI contribution to the report
  • textReferences content: Engineer A did not cite their use of AI-software or its large language models, and submitted the draft report to Client W for review, including language to clearly identify that the supplied report was a draft, but applied their seal consistent with state law.
  • temporalExtent content: instant
  • temporalSequence content: 9
  • withinCompetence assessment: True
RDF JSON-LD
{
  "@context": {
    "proeth": "http://proethica.org/ontology/intermediate#",
    "proeth-case": "http://proethica.org/ontology/case/7#",
    "rdf": "http://www.w3.org/1999/02/22-rdf-syntax-ns#",
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    "time": "http://www.w3.org/2006/time#"
  },
  "@id": "http://proethica.org/ontology/case/7#Action_Report_Sealing_and_Submission",
  "@type": "proeth:Action",
  "proeth:description": "Engineer A submitted the draft report to Client W for review, marking it clearly as a draft and applying their seal consistent with state law, but chose not to cite the use of AI software or its large language models.",
  "proeth:eventRoleContext": "Licensed Environmental Engineer",
  "proeth:foreseenUnintendedEffects": [
    "Client unaware of the extent of AI contribution to the report"
  ],
  "proeth:fulfillsObligation": [],
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  "proeth:hasMentalState": "deliberate",
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    "Undisclosed AI Contribution to Report"
  ],
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  "proeth:raisesObligation": [],
  "proeth:requiresCapability": [
    "Authority to seal engineering documents",
    "Environmental engineering expertise"
  ],
  "proeth:temporalExtent": "instant",
  "proeth:temporalMarker": "At report submission to Client W",
  "proeth:temporalSequence": 9,
  "proeth:textReferences": [
    "Engineer A did not cite their use of AI-software or its large language models, and submitted the draft report to Client W for review, including language to clearly identify that the supplied report was a draft, but applied their seal consistent with state law."
  ],
  "proeth:violatesObligation": [
    "Give Credit for Engineering Work (Code III.9)"
  ],
  "proeth:withinCompetence": true,
  "rdfs:label": "Report Sealing and Submission"
}

Description: Engineer A did not cite or disclose the AI-assisted drafting tools used to generate the engineering design documents when providing them to Client W.

Temporal Marker: At design document submission instant

Fluent Transitions:
Initiates (1)
  • Undisclosed AI Contribution to Design Documents

Mental State: deliberate

Intended Outcome: Present the design documents as Engineer A's work product without noting the AI tools used

Foreseen Unintended Effects:

  • Client misunderstanding about the origin and reliability of the design work
Obligation Engagement:
Violates (1)
  • Give Credit for Engineering Work (Code III.9)
Required Capabilities:
Professional transparency in attribution
Within Competence: Yes
Field classification (triples vs literals)
Relations (structural triples)
  • violatesObligation: Give Credit for Engineering Work (Code III.9)
  • requiresCapability: Professional transparency in attribution
  • initiates: Undisclosed AI Contribution to Design Documents
Literal extractions (kept for synthesis)
  • description content: Engineer A did not cite or disclose the AI-assisted drafting tools used to generate the engineering design documents when providing them to Client W.
  • hasAgent content: Engineer A
  • temporalMarker content: At design document submission
  • eventRoleContext content: Licensed Environmental Engineer
  • hasMentalState content: deliberate
  • intendedOutcome content: Present the design documents as Engineer A's work product without noting the AI tools used
  • foreseenUnintendedEffects content: Client misunderstanding about the origin and reliability of the design work
  • textReferences content: Again, Engineer A did not cite the AI-assisted drafting tools they used to generate the engineering design documents.
  • temporalExtent content: instant
  • temporalSequence content: 10
  • withinCompetence assessment: True
RDF JSON-LD
{
  "@context": {
    "proeth": "http://proethica.org/ontology/intermediate#",
    "proeth-case": "http://proethica.org/ontology/case/7#",
    "rdf": "http://www.w3.org/1999/02/22-rdf-syntax-ns#",
    "rdfs": "http://www.w3.org/2000/01/rdf-schema#",
    "time": "http://www.w3.org/2006/time#"
  },
  "@id": "http://proethica.org/ontology/case/7#Action_Design_AI_Disclosure_Omission",
  "@type": "proeth:Action",
  "proeth:description": "Engineer A did not cite or disclose the AI-assisted drafting tools used to generate the engineering design documents when providing them to Client W.",
  "proeth:eventRoleContext": "Licensed Environmental Engineer",
  "proeth:foreseenUnintendedEffects": [
    "Client misunderstanding about the origin and reliability of the design work"
  ],
  "proeth:fulfillsObligation": [],
  "proeth:hasAgent": "Engineer A",
  "proeth:hasMentalState": "deliberate",
  "proeth:initiates": [
    "Undisclosed AI Contribution to Design Documents"
  ],
  "proeth:intendedOutcome": "Present the design documents as Engineer A\u0027s work product without noting the AI tools used",
  "proeth:raisesObligation": [],
  "proeth:requiresCapability": [
    "Professional transparency in attribution"
  ],
  "proeth:temporalExtent": "instant",
  "proeth:temporalMarker": "At design document submission",
  "proeth:temporalSequence": 10,
  "proeth:textReferences": [
    "Again, Engineer A did not cite the AI-assisted drafting tools they used to generate the engineering design documents."
  ],
  "proeth:violatesObligation": [
    "Give Credit for Engineering Work (Code III.9)"
  ],
  "proeth:withinCompetence": true,
  "rdfs:label": "Design AI Disclosure Omission"
}

Description: After identifying misaligned dimensions and omitted safety features required by local regulations, Client W raised concerns about the design's accuracy and reliability and instructed Engineer A to revise the plans to meet professional and regulatory standards.

Temporal Marker: After Client W's review of the deliverables instant

Fluent Transitions:
Initiates (1)
  • Design Revision Required

Mental State: deliberate

Intended Outcome: Obtain corrected design documents that satisfy all necessary professional and regulatory standards

Guided By Principles:
  • Hold Paramount Public Safety, Health, and Welfare
Required Capabilities:
Ability to review deliverables against regulatory requirements
Within Competence: Yes
Field classification (triples vs literals)
Relations (structural triples)
  • guidedByPrinciple: Hold Paramount Public Safety, Health, and Welfare
  • requiresCapability: Ability to review deliverables against regulatory requirements
  • initiates: Design Revision Required
Literal extractions (kept for synthesis)
  • description content: After identifying misaligned dimensions and omitted safety features required by local regulations, Client W raised concerns about the design's accuracy and reliability and instructed Engineer A to revise the plans to meet professional and regulatory standards.
  • hasAgent content: Client W
  • temporalMarker content: After Client W's review of the deliverables
  • eventRoleContext content: Client
  • hasMentalState content: deliberate
  • intendedOutcome content: Obtain corrected design documents that satisfy all necessary professional and regulatory standards
  • textReferences content: Client W raised concerns about the accuracy and reliability of the engineering design and instructed Engineer A to revise the plans, ensuring that all elements satisfied the necessary professional and regulatory standards.
  • temporalExtent content: instant
  • temporalSequence content: 13
  • withinCompetence assessment: True
RDF JSON-LD
{
  "@context": {
    "proeth": "http://proethica.org/ontology/intermediate#",
    "proeth-case": "http://proethica.org/ontology/case/7#",
    "rdf": "http://www.w3.org/1999/02/22-rdf-syntax-ns#",
    "rdfs": "http://www.w3.org/2000/01/rdf-schema#",
    "time": "http://www.w3.org/2006/time#"
  },
  "@id": "http://proethica.org/ontology/case/7#Action_Revision_Instruction",
  "@type": "proeth:Action",
  "proeth:description": "After identifying misaligned dimensions and omitted safety features required by local regulations, Client W raised concerns about the design\u0027s accuracy and reliability and instructed Engineer A to revise the plans to meet professional and regulatory standards.",
  "proeth:eventRoleContext": "Client",
  "proeth:guidedByPrinciple": [
    "Hold Paramount Public Safety, Health, and Welfare"
  ],
  "proeth:hasAgent": "Client W",
  "proeth:hasMentalState": "deliberate",
  "proeth:initiates": [
    "Design Revision Required"
  ],
  "proeth:intendedOutcome": "Obtain corrected design documents that satisfy all necessary professional and regulatory standards",
  "proeth:requiresCapability": [
    "Ability to review deliverables against regulatory requirements"
  ],
  "proeth:temporalExtent": "instant",
  "proeth:temporalMarker": "After Client W\u0027s review of the deliverables",
  "proeth:temporalSequence": 13,
  "proeth:textReferences": [
    "Client W raised concerns about the accuracy and reliability of the engineering design and instructed Engineer A to revise the plans, ensuring that all elements satisfied the necessary professional and regulatory standards."
  ],
  "proeth:withinCompetence": true,
  "rdfs:label": "Revision Instruction"
}
Extracted Events (6)
Occurrences that trigger ethical considerations and state changes

Description: Engineer B, Engineer A's mentor and supervisor who previously provided quality assurance reviews, retired and became unavailable to Engineer A in a work capacity.

Temporal Marker: Before Engineer A was retained by Client W instant

Fluent Transitions:
Initiates (2)
  • Loss of Mentorship Support
  • Unreviewed Work Product Risk
Terminates (1)
  • Mentor Quality Assurance Available
Field classification (triples vs literals)
Relations (structural triples)
  • initiates: Loss of Mentorship Support; Unreviewed Work Product Risk
  • terminates: Mentor Quality Assurance Available
Literal extractions (kept for synthesis)
  • description content: Engineer B, Engineer A's mentor and supervisor who previously provided quality assurance reviews, retired and became unavailable to Engineer A in a work capacity.
  • temporalMarker content: Before Engineer A was retained by Client W
  • textReferences content: But Engineer B recently retired and was no longer available to Engineer A in a work capacity.
  • eventType content: exogenous
  • temporalExtent content: instant
  • temporalSequence content: 1
  • confidence assessment: 0.95
RDF JSON-LD
{
  "@context": {
    "proeth": "http://proethica.org/ontology/intermediate#",
    "proeth-case": "http://proethica.org/ontology/case/7#",
    "rdf": "http://www.w3.org/1999/02/22-rdf-syntax-ns#",
    "rdfs": "http://www.w3.org/2000/01/rdf-schema#",
    "time": "http://www.w3.org/2006/time#"
  },
  "@id": "http://proethica.org/ontology/case/7#Event_Mentor_Retirement",
  "@type": "proeth:Event",
  "proeth:confidence": "0.95",
  "proeth:description": "Engineer B, Engineer A\u0027s mentor and supervisor who previously provided quality assurance reviews, retired and became unavailable to Engineer A in a work capacity.",
  "proeth:eventType": "exogenous",
  "proeth:initiates": [
    "Loss of Mentorship Support",
    "Unreviewed Work Product Risk"
  ],
  "proeth:temporalExtent": "instant",
  "proeth:temporalMarker": "Before Engineer A was retained by Client W",
  "proeth:temporalSequence": 1,
  "proeth:terminates": [
    "Mentor Quality Assurance Available"
  ],
  "proeth:textReferences": [
    "But Engineer B recently retired and was no longer available to Engineer A in a work capacity."
  ],
  "rdfs:label": "Mentor Retirement"
}

Description: Uploading Client W's private information into the open-source AI interface placed the client's confidential information into the public domain without prior consent from Client W.

Temporal Marker: At the moment Engineer A input Client W's information into the AI software instant

Fluent Transitions:
Initiates (1)
  • Client Confidentiality Breach
Terminates (1)
  • Client Information Confidentiality

Caused By Action: Action_Confidential_Data_Input

Field classification (triples vs literals)
Relations (structural triples)
  • causedByAction: http://proethica.org/ontology/case/7#Action_Confidential_Data_Input
  • initiates: Client Confidentiality Breach
  • terminates: Client Information Confidentiality
Literal extractions (kept for synthesis)
  • description content: Uploading Client W's private information into the open-source AI interface placed the client's confidential information into the public domain without prior consent from Client W.
  • temporalMarker content: At the moment Engineer A input Client W's information into the AI software
  • textReferences content: When Engineer A uploaded Client W’s information into the AI open-source interface, this was tantamount to placing the Client’s private information in the public domain.
  • eventType content: automatic
  • temporalExtent content: instant
  • temporalSequence content: 4
  • confidence assessment: 0.9
RDF JSON-LD
{
  "@context": {
    "proeth": "http://proethica.org/ontology/intermediate#",
    "proeth-case": "http://proethica.org/ontology/case/7#",
    "rdf": "http://www.w3.org/1999/02/22-rdf-syntax-ns#",
    "rdfs": "http://www.w3.org/2000/01/rdf-schema#",
    "time": "http://www.w3.org/2006/time#"
  },
  "@id": "http://proethica.org/ontology/case/7#Event_Confidential_Information_Exposure",
  "@type": "proeth:Event",
  "proeth:causedByAction": "http://proethica.org/ontology/case/7#Action_Confidential_Data_Input",
  "proeth:confidence": "0.9",
  "proeth:description": "Uploading Client W\u0027s private information into the open-source AI interface placed the client\u0027s confidential information into the public domain without prior consent from Client W.",
  "proeth:eventType": "automatic",
  "proeth:initiates": [
    "Client Confidentiality Breach"
  ],
  "proeth:temporalExtent": "instant",
  "proeth:temporalMarker": "At the moment Engineer A input Client W\u0027s information into the AI software",
  "proeth:temporalSequence": 4,
  "proeth:terminates": [
    "Client Information Confidentiality"
  ],
  "proeth:textReferences": [
    "When Engineer A uploaded Client W\u2019s information into the AI open-source interface, this was tantamount to placing the Client\u2019s private information in the public domain."
  ],
  "rdfs:label": "Confidential Information Exposure"
}

Description: The open-sourced AI software synthesized the information Engineer A input and produced a first draft of the comprehensive contaminant report after a few refining prompts.

Temporal Marker: After Engineer A input Client W's information into the AI software instant

Fluent Transitions:
Initiates (2)
  • AI-Generated Report Draft Exists
  • Uncited AI Contribution

Caused By Action: Action_Confidential_Data_Input

Field classification (triples vs literals)
Relations (structural triples)
  • causedByAction: http://proethica.org/ontology/case/7#Action_Confidential_Data_Input
  • initiates: AI-Generated Report Draft Exists; Uncited AI Contribution
Literal extractions (kept for synthesis)
  • description content: The open-sourced AI software synthesized the information Engineer A input and produced a first draft of the comprehensive contaminant report after a few refining prompts.
  • temporalMarker content: After Engineer A input Client W's information into the AI software
  • textReferences content: after a few refining prompts, received a first draft of the report generated by the AI software
  • eventType content: automatic
  • temporalExtent content: instant
  • temporalSequence content: 5
  • confidence assessment: 0.93
RDF JSON-LD
{
  "@context": {
    "proeth": "http://proethica.org/ontology/intermediate#",
    "proeth-case": "http://proethica.org/ontology/case/7#",
    "rdf": "http://www.w3.org/1999/02/22-rdf-syntax-ns#",
    "rdfs": "http://www.w3.org/2000/01/rdf-schema#",
    "time": "http://www.w3.org/2006/time#"
  },
  "@id": "http://proethica.org/ontology/case/7#Event_AI_Report_Generation",
  "@type": "proeth:Event",
  "proeth:causedByAction": "http://proethica.org/ontology/case/7#Action_Confidential_Data_Input",
  "proeth:confidence": "0.93",
  "proeth:description": "The open-sourced AI software synthesized the information Engineer A input and produced a first draft of the comprehensive contaminant report after a few refining prompts.",
  "proeth:eventType": "automatic",
  "proeth:initiates": [
    "AI-Generated Report Draft Exists",
    "Uncited AI Contribution"
  ],
  "proeth:temporalExtent": "instant",
  "proeth:temporalMarker": "After Engineer A input Client W\u0027s information into the AI software",
  "proeth:temporalSequence": 5,
  "proeth:textReferences": [
    "after a few refining prompts, received a first draft of the report generated by the AI software"
  ],
  "rdfs:label": "AI Report Generation"
}

Description: The AI-assisted drafting tools generated a preliminary design of the plans, including basic layouts and technical specifications, that contained misaligned dimensions and omitted key safety features required by local regulations.

Temporal Marker: After Engineer A entered Client W's information into the AI drafting software instant

Fluent Transitions:
Initiates (3)
  • AI-Generated Design Documents Exist
  • Latent Design Errors
  • Public Safety Risk

Caused By Action: Action_AI_Tool_Adoption

Field classification (triples vs literals)
Relations (structural triples)
  • causedByAction: http://proethica.org/ontology/case/7#Action_AI_Tool_Adoption
  • initiates: AI-Generated Design Documents Exist; Latent Design Errors; Public Safety Risk
Literal extractions (kept for synthesis)
  • description content: The AI-assisted drafting tools generated a preliminary design of the plans, including basic layouts and technical specifications, that contained misaligned dimensions and omitted key safety features required by local regulations.
  • temporalMarker content: After Engineer A entered Client W's information into the AI drafting software
  • textReferences content: relied on the AI-assisted drafting tools to generate a preliminary design of the plans, including basic layouts and technical specifications
  • eventType content: automatic
  • temporalExtent content: instant
  • temporalSequence content: 6
  • confidence assessment: 0.92
RDF JSON-LD
{
  "@context": {
    "proeth": "http://proethica.org/ontology/intermediate#",
    "proeth-case": "http://proethica.org/ontology/case/7#",
    "rdf": "http://www.w3.org/1999/02/22-rdf-syntax-ns#",
    "rdfs": "http://www.w3.org/2000/01/rdf-schema#",
    "time": "http://www.w3.org/2006/time#"
  },
  "@id": "http://proethica.org/ontology/case/7#Event_AI_Design_Generation",
  "@type": "proeth:Event",
  "proeth:causedByAction": "http://proethica.org/ontology/case/7#Action_AI_Tool_Adoption",
  "proeth:confidence": "0.92",
  "proeth:description": "The AI-assisted drafting tools generated a preliminary design of the plans, including basic layouts and technical specifications, that contained misaligned dimensions and omitted key safety features required by local regulations.",
  "proeth:eventType": "automatic",
  "proeth:initiates": [
    "AI-Generated Design Documents Exist",
    "Latent Design Errors",
    "Public Safety Risk"
  ],
  "proeth:temporalExtent": "instant",
  "proeth:temporalMarker": "After Engineer A entered Client W\u0027s information into the AI drafting software",
  "proeth:temporalSequence": 6,
  "proeth:textReferences": [
    "relied on the AI-assisted drafting tools to generate a preliminary design of the plans, including basic layouts and technical specifications"
  ],
  "rdfs:label": "AI Design Generation"
}

Description: During Client W's review, the client noted the report read as if written by two different authors, with the AI-generated introduction exceptionally polished and the data analysis section needing minor edits, though the report was otherwise satisfactory.

Temporal Marker: When Client W reviewed the draft report instant

Fluent Transitions:
Initiates (1)
  • Client Transparency Concern

Caused By Action: Action_Report_Sealing_and_Submission

Field classification (triples vs literals)
Relations (structural triples)
  • causedByAction: http://proethica.org/ontology/case/7#Action_Report_Sealing_and_Submission
  • initiates: Client Transparency Concern
Literal extractions (kept for synthesis)
  • description content: During Client W's review, the client noted the report read as if written by two different authors, with the AI-generated introduction exceptionally polished and the data analysis section needing minor edits, though the report was otherwise satisfactory.
  • temporalMarker content: When Client W reviewed the draft report
  • textReferences content: The Client commented that the report read as if written by two different authors but was otherwise satisfactory.
  • eventType content: outcome
  • temporalExtent content: instant
  • temporalSequence content: 11
  • confidence assessment: 0.88
RDF JSON-LD
{
  "@context": {
    "proeth": "http://proethica.org/ontology/intermediate#",
    "proeth-case": "http://proethica.org/ontology/case/7#",
    "rdf": "http://www.w3.org/1999/02/22-rdf-syntax-ns#",
    "rdfs": "http://www.w3.org/2000/01/rdf-schema#",
    "time": "http://www.w3.org/2006/time#"
  },
  "@id": "http://proethica.org/ontology/case/7#Event_Report_Inconsistency_Observation",
  "@type": "proeth:Event",
  "proeth:causedByAction": "http://proethica.org/ontology/case/7#Action_Report_Sealing_and_Submission",
  "proeth:confidence": "0.88",
  "proeth:description": "During Client W\u0027s review, the client noted the report read as if written by two different authors, with the AI-generated introduction exceptionally polished and the data analysis section needing minor edits, though the report was otherwise satisfactory.",
  "proeth:eventType": "outcome",
  "proeth:initiates": [
    "Client Transparency Concern"
  ],
  "proeth:temporalExtent": "instant",
  "proeth:temporalMarker": "When Client W reviewed the draft report",
  "proeth:temporalSequence": 11,
  "proeth:textReferences": [
    "The Client commented that the report read as if written by two different authors but was otherwise satisfactory."
  ],
  "rdfs:label": "Report Inconsistency Observation"
}

Description: Client W discovered misaligned dimensions and an omission of key safety features required by local regulations in the AI-generated design documents, errors that Engineer A's cursory review had failed to detect.

Temporal Marker: When Client W reviewed the design documents instant

Fluent Transitions:
Initiates (3)
  • Known Design Deficiency
  • Regulatory Noncompliance Risk
  • Client Reliability Concern
Terminates (1)
  • Latent Design Errors

Caused By Action: Action_Cursory_Design_Review

Field classification (triples vs literals)
Relations (structural triples)
  • causedByAction: http://proethica.org/ontology/case/7#Action_Cursory_Design_Review
  • initiates: Known Design Deficiency; Regulatory Noncompliance Risk; Client Reliability Concern
  • terminates: Latent Design Errors
Literal extractions (kept for synthesis)
  • description content: Client W discovered misaligned dimensions and an omission of key safety features required by local regulations in the AI-generated design documents, errors that Engineer A's cursory review had failed to detect.
  • temporalMarker content: When Client W reviewed the design documents
  • textReferences content: Client W, however, noticed several issues with the AI-generated design documents, including misaligned dimensions and an omission of key safety features required by local regulations.
  • eventType content: outcome
  • temporalExtent content: instant
  • temporalSequence content: 12
  • confidence assessment: 0.93
RDF JSON-LD
{
  "@context": {
    "proeth": "http://proethica.org/ontology/intermediate#",
    "proeth-case": "http://proethica.org/ontology/case/7#",
    "rdf": "http://www.w3.org/1999/02/22-rdf-syntax-ns#",
    "rdfs": "http://www.w3.org/2000/01/rdf-schema#",
    "time": "http://www.w3.org/2006/time#"
  },
  "@id": "http://proethica.org/ontology/case/7#Event_Design_Error_Discovery",
  "@type": "proeth:Event",
  "proeth:causedByAction": "http://proethica.org/ontology/case/7#Action_Cursory_Design_Review",
  "proeth:confidence": "0.93",
  "proeth:description": "Client W discovered misaligned dimensions and an omission of key safety features required by local regulations in the AI-generated design documents, errors that Engineer A\u0027s cursory review had failed to detect.",
  "proeth:eventType": "outcome",
  "proeth:initiates": [
    "Known Design Deficiency",
    "Regulatory Noncompliance Risk",
    "Client Reliability Concern"
  ],
  "proeth:temporalExtent": "instant",
  "proeth:temporalMarker": "When Client W reviewed the design documents",
  "proeth:temporalSequence": 12,
  "proeth:terminates": [
    "Latent Design Errors"
  ],
  "proeth:textReferences": [
    "Client W, however, noticed several issues with the AI-generated design documents, including misaligned dimensions and an omission of key safety features required by local regulations."
  ],
  "rdfs:label": "Design Error Discovery"
}
Causal Chains (8)
NESS test analysis: Necessary Element of Sufficient Set

Causal Language: Faced with the need to deliver both the report and the engineering design documents without the review by and mentorship from Engineer B, Engineer A opted to use open-sourced artificial intelligence (AI) software to create an initial draft of the necessary report and to use AI-assisted drafting tools to generate preliminary design documents.

Necessary Factors (NESS):
  • Engineer B's retirement removing quality assurance and mentorship support
  • Engineer A's lack of confidence in their own technical writing
  • Obligation to deliver both the report and design documents to Client W
Sufficient Factors:
  • Combination of lost mentorship + writing insecurity + delivery obligation was enough to prompt the turn to AI tools
Counterfactual Test: Had Engineer B remained available for review and mentorship, Engineer A would likely have followed the prior workflow of drafting and receiving quality assurance review rather than adopting an unfamiliar AI tool
Responsibility Attribution:

Agent: Engineer A
Type: direct
Within Agent Control: Yes

Causal Sequence:
  1. Mentor Retirement
    Engineer B retires and is no longer available to Engineer A in a work capacity
  2. Loss of Quality Assurance Support
    Engineer A can no longer rely on guidance and quality assurance reviews to refine report drafts
  3. Delivery Pressure
    Engineer A still must deliver both the report and the engineering design documents
  4. AI Tool Adoption
    Engineer A opts to use open-sourced AI software and AI-assisted drafting tools despite having no previous experience with them
Field classification (triples vs literals)
Relations (structural triples)
  • cause: Mentor Retirement
  • effect: AI Tool Adoption
  • responsibleAgent: Engineer A
Literal extractions (kept for synthesis)
  • causalLanguage content: Faced with the need to deliver both the report and the engineering design documents without the review by and mentorship from Engineer B, Engineer A opted to use open-sourced artificial intelligence (AI) software to create an initial draft of the necessary report and to use AI-assisted drafting tools to generate preliminary design documents.
  • necessaryFactors content: Engineer B's retirement removing quality assurance and mentorship support; Engineer A's lack of confidence in their own technical writing; Obligation to deliver both the report and design documents to Client W
  • sufficientFactors content: Combination of lost mentorship + writing insecurity + delivery obligation was enough to prompt the turn to AI tools
  • counterfactual content: Had Engineer B remained available for review and mentorship, Engineer A would likely have followed the prior workflow of drafting and receiving quality assurance review rather than adopting an unfamiliar AI tool
  • causalSequence content: {'proeth:step': 1, 'proeth:element': 'Mentor Retirement', 'proeth:description': 'Engineer B retires and is no longer available to Engineer A in a work capacity'}; {'proeth:step': 2, 'proeth:element': 'Loss of Quality Assurance Support', 'proeth:description': 'Engineer A can no longer rely on guidance and quality assurance reviews to refine report drafts'}; {'proeth:step': 3, 'proeth:element': 'Delivery Pressure', 'proeth:description': 'Engineer A still must deliver both the report and the engineering design documents'}; {'proeth:step': 4, 'proeth:element': 'AI Tool Adoption', 'proeth:description': 'Engineer A opts to use open-sourced AI software and AI-assisted drafting tools despite having no previous experience with them'}
  • responsibilityType assessment: direct
  • withinAgentControl assessment: True
RDF JSON-LD
{
  "@context": {
    "proeth": "http://proethica.org/ontology/intermediate#",
    "proeth-case": "http://proethica.org/ontology/case/7#",
    "rdf": "http://www.w3.org/1999/02/22-rdf-syntax-ns#",
    "rdfs": "http://www.w3.org/2000/01/rdf-schema#"
  },
  "@id": "http://proethica.org/ontology/case/7#CausalChain_1",
  "@type": "proeth:CausalChain",
  "proeth:causalLanguage": "Faced with the need to deliver both the report and the engineering design documents without the review by and mentorship from Engineer B, Engineer A opted to use open-sourced artificial intelligence (AI) software to create an initial draft of the necessary report and to use AI-assisted drafting tools to generate preliminary design documents.",
  "proeth:causalSequence": [
    {
      "proeth:description": "Engineer B retires and is no longer available to Engineer A in a work capacity",
      "proeth:element": "Mentor Retirement",
      "proeth:step": 1
    },
    {
      "proeth:description": "Engineer A can no longer rely on guidance and quality assurance reviews to refine report drafts",
      "proeth:element": "Loss of Quality Assurance Support",
      "proeth:step": 2
    },
    {
      "proeth:description": "Engineer A still must deliver both the report and the engineering design documents",
      "proeth:element": "Delivery Pressure",
      "proeth:step": 3
    },
    {
      "proeth:description": "Engineer A opts to use open-sourced AI software and AI-assisted drafting tools despite having no previous experience with them",
      "proeth:element": "AI Tool Adoption",
      "proeth:step": 4
    }
  ],
  "proeth:cause": "Mentor Retirement",
  "proeth:counterfactual": "Had Engineer B remained available for review and mentorship, Engineer A would likely have followed the prior workflow of drafting and receiving quality assurance review rather than adopting an unfamiliar AI tool",
  "proeth:discoveredInSection": "facts",
  "proeth:effect": "AI Tool Adoption",
  "proeth:necessaryFactors": [
    "Engineer B\u0027s retirement removing quality assurance and mentorship support",
    "Engineer A\u0027s lack of confidence in their own technical writing",
    "Obligation to deliver both the report and design documents to Client W"
  ],
  "proeth:responsibilityType": "direct",
  "proeth:responsibleAgent": "Engineer A",
  "proeth:sufficientFactors": [
    "Combination of lost mentorship + writing insecurity + delivery obligation was enough to prompt the turn to AI tools"
  ],
  "proeth:withinAgentControl": true
}

Causal Language: Engineer A input the information gathered from Client W into the AI software, and, after a few refining prompts, received a first draft of the report generated by the AI software.

Necessary Factors (NESS):
  • Decision to use the open-source AI software as the drafting mechanism
  • Need to supply client-provided information for the AI to synthesize a draft
Sufficient Factors:
  • The chosen AI workflow required inputting Client W's information to generate content, making the upload a direct consequence of the adoption decision
Counterfactual Test: If Engineer A had drafted manually or used a closed, secure tool, Client W's information would not have been entered into an open-source interface
Responsibility Attribution:

Agent: Engineer A
Type: direct
Within Agent Control: Yes

Causal Sequence:
  1. AI Tool Adoption
    Engineer A opts to use open-sourced AI software for the report and design documents
  2. Data Gathering
    Engineer A gathers the relevant information provided by Client W
  3. Confidential Data Input
    Engineer A inputs Client W's information into the open-source AI software to generate drafts
Field classification (triples vs literals)
Relations (structural triples)
  • cause: AI Tool Adoption
  • effect: Confidential Data Input
  • responsibleAgent: Engineer A
Literal extractions (kept for synthesis)
  • causalLanguage content: Engineer A input the information gathered from Client W into the AI software, and, after a few refining prompts, received a first draft of the report generated by the AI software.
  • necessaryFactors content: Decision to use the open-source AI software as the drafting mechanism; Need to supply client-provided information for the AI to synthesize a draft
  • sufficientFactors content: The chosen AI workflow required inputting Client W's information to generate content, making the upload a direct consequence of the adoption decision
  • counterfactual content: If Engineer A had drafted manually or used a closed, secure tool, Client W's information would not have been entered into an open-source interface
  • causalSequence content: {'proeth:step': 1, 'proeth:element': 'AI Tool Adoption', 'proeth:description': 'Engineer A opts to use open-sourced AI software for the report and design documents'}; {'proeth:step': 2, 'proeth:element': 'Data Gathering', 'proeth:description': 'Engineer A gathers the relevant information provided by Client W'}; {'proeth:step': 3, 'proeth:element': 'Confidential Data Input', 'proeth:description': "Engineer A inputs Client W's information into the open-source AI software to generate drafts"}
  • responsibilityType assessment: direct
  • withinAgentControl assessment: True
RDF JSON-LD
{
  "@context": {
    "proeth": "http://proethica.org/ontology/intermediate#",
    "proeth-case": "http://proethica.org/ontology/case/7#",
    "rdf": "http://www.w3.org/1999/02/22-rdf-syntax-ns#",
    "rdfs": "http://www.w3.org/2000/01/rdf-schema#"
  },
  "@id": "http://proethica.org/ontology/case/7#CausalChain_2",
  "@type": "proeth:CausalChain",
  "proeth:causalLanguage": "Engineer A input the information gathered from Client W into the AI software, and, after a few refining prompts, received a first draft of the report generated by the AI software.",
  "proeth:causalSequence": [
    {
      "proeth:description": "Engineer A opts to use open-sourced AI software for the report and design documents",
      "proeth:element": "AI Tool Adoption",
      "proeth:step": 1
    },
    {
      "proeth:description": "Engineer A gathers the relevant information provided by Client W",
      "proeth:element": "Data Gathering",
      "proeth:step": 2
    },
    {
      "proeth:description": "Engineer A inputs Client W\u0027s information into the open-source AI software to generate drafts",
      "proeth:element": "Confidential Data Input",
      "proeth:step": 3
    }
  ],
  "proeth:cause": "AI Tool Adoption",
  "proeth:counterfactual": "If Engineer A had drafted manually or used a closed, secure tool, Client W\u0027s information would not have been entered into an open-source interface",
  "proeth:discoveredInSection": "facts",
  "proeth:effect": "Confidential Data Input",
  "proeth:necessaryFactors": [
    "Decision to use the open-source AI software as the drafting mechanism",
    "Need to supply client-provided information for the AI to synthesize a draft"
  ],
  "proeth:responsibilityType": "direct",
  "proeth:responsibleAgent": "Engineer A",
  "proeth:sufficientFactors": [
    "The chosen AI workflow required inputting Client W\u0027s information to generate content, making the upload a direct consequence of the adoption decision"
  ],
  "proeth:withinAgentControl": true
}

Causal Language: When Engineer A uploaded Client W’s information into the AI open-source interface, this was tantamount to placing the Client’s private information in the public domain.

Necessary Factors (NESS):
  • Uploading Client W's private information into the AI interface
  • The open-source nature of the AI software
  • Absence of Client W's prior consent
Sufficient Factors:
  • Uploading confidential client data into an open-source interface was itself sufficient to place the information in the public domain
Counterfactual Test: Without the upload to the open-source interface (or with a secure, private tool), Client W's confidential information would not have been exposed
Responsibility Attribution:

Agent: Engineer A
Type: direct
Within Agent Control: Yes

Causal Sequence:
  1. Confidential Data Input
    Engineer A enters Client W's private information into the open-source AI interface
  2. Open-Source Processing
    The open-source system processes and retains the client's data outside a protected environment
  3. Confidential Information Exposure
    Client W's private information is effectively placed in the public domain without consent, contrary to Code section II.1.c
Field classification (triples vs literals)
Relations (structural triples)
  • cause: Confidential Data Input
  • effect: Confidential Information Exposure
  • responsibleAgent: Engineer A
Literal extractions (kept for synthesis)
  • causalLanguage content: When Engineer A uploaded Client W’s information into the AI open-source interface, this was tantamount to placing the Client’s private information in the public domain.
  • necessaryFactors content: Uploading Client W's private information into the AI interface; The open-source nature of the AI software; Absence of Client W's prior consent
  • sufficientFactors content: Uploading confidential client data into an open-source interface was itself sufficient to place the information in the public domain
  • counterfactual content: Without the upload to the open-source interface (or with a secure, private tool), Client W's confidential information would not have been exposed
  • causalSequence content: {'proeth:step': 1, 'proeth:element': 'Confidential Data Input', 'proeth:description': "Engineer A enters Client W's private information into the open-source AI interface"}; {'proeth:step': 2, 'proeth:element': 'Open-Source Processing', 'proeth:description': "The open-source system processes and retains the client's data outside a protected environment"}; {'proeth:step': 3, 'proeth:element': 'Confidential Information Exposure', 'proeth:description': "Client W's private information is effectively placed in the public domain without consent, contrary to Code section II.1.c"}
  • responsibilityType assessment: direct
  • withinAgentControl assessment: True
RDF JSON-LD
{
  "@context": {
    "proeth": "http://proethica.org/ontology/intermediate#",
    "proeth-case": "http://proethica.org/ontology/case/7#",
    "rdf": "http://www.w3.org/1999/02/22-rdf-syntax-ns#",
    "rdfs": "http://www.w3.org/2000/01/rdf-schema#"
  },
  "@id": "http://proethica.org/ontology/case/7#CausalChain_3",
  "@type": "proeth:CausalChain",
  "proeth:causalLanguage": "When Engineer A uploaded Client W\u2019s information into the AI open-source interface, this was tantamount to placing the Client\u2019s private information in the public domain.",
  "proeth:causalSequence": [
    {
      "proeth:description": "Engineer A enters Client W\u0027s private information into the open-source AI interface",
      "proeth:element": "Confidential Data Input",
      "proeth:step": 1
    },
    {
      "proeth:description": "The open-source system processes and retains the client\u0027s data outside a protected environment",
      "proeth:element": "Open-Source Processing",
      "proeth:step": 2
    },
    {
      "proeth:description": "Client W\u0027s private information is effectively placed in the public domain without consent, contrary to Code section II.1.c",
      "proeth:element": "Confidential Information Exposure",
      "proeth:step": 3
    }
  ],
  "proeth:cause": "Confidential Data Input",
  "proeth:counterfactual": "Without the upload to the open-source interface (or with a secure, private tool), Client W\u0027s confidential information would not have been exposed",
  "proeth:discoveredInSection": "discussion",
  "proeth:effect": "Confidential Information Exposure",
  "proeth:necessaryFactors": [
    "Uploading Client W\u0027s private information into the AI interface",
    "The open-source nature of the AI software",
    "Absence of Client W\u0027s prior consent"
  ],
  "proeth:responsibilityType": "direct",
  "proeth:responsibleAgent": "Engineer A",
  "proeth:sufficientFactors": [
    "Uploading confidential client data into an open-source interface was itself sufficient to place the information in the public domain"
  ],
  "proeth:withinAgentControl": true
}

Causal Language: Not being familiar with the full functionality of the AI software, including the accuracy and originality of AI-generated text, Engineer A conducted a thorough review of the report, cross-checking key facts against professional journal articles and verifying the phrasing by running search engine queries to ensure the content did not match any existing language.

Necessary Factors (NESS):
  • Existence of the AI-generated first draft
  • Engineer A's awareness of unfamiliarity with the AI tool's accuracy and originality
  • Engineer A's technical competence to verify content
Sufficient Factors:
  • AI-generated draft of uncertain accuracy + Engineer A's professional diligence was enough to prompt cross-checking against professional sources
Counterfactual Test: Without the AI-generated draft, this specific verification exercise (fact cross-checking and plagiarism searches) would not have occurred; without Engineer A's diligence, the review might have been cursory as with the designs
Responsibility Attribution:

Agent: Engineer A
Type: direct
Within Agent Control: Yes

Causal Sequence:
  1. AI Report Generation
    The AI software synthesizes Client W's information and produces a first draft of the report
  2. Recognition of Uncertainty
    Engineer A, unfamiliar with the tool's accuracy and originality, distrusts the raw output
  3. Thorough Report Review
    Engineer A cross-checks key facts against professional journal articles and verifies phrasing via search queries, then personalizes wording
Field classification (triples vs literals)
Relations (structural triples)
  • cause: AI Report Generation
  • effect: Thorough Report Review
  • responsibleAgent: Engineer A
Literal extractions (kept for synthesis)
  • causalLanguage content: Not being familiar with the full functionality of the AI software, including the accuracy and originality of AI-generated text, Engineer A conducted a thorough review of the report, cross-checking key facts against professional journal articles and verifying the phrasing by running search engine queries to ensure the content did not match any existing language.
  • necessaryFactors content: Existence of the AI-generated first draft; Engineer A's awareness of unfamiliarity with the AI tool's accuracy and originality; Engineer A's technical competence to verify content
  • sufficientFactors content: AI-generated draft of uncertain accuracy + Engineer A's professional diligence was enough to prompt cross-checking against professional sources
  • counterfactual content: Without the AI-generated draft, this specific verification exercise (fact cross-checking and plagiarism searches) would not have occurred; without Engineer A's diligence, the review might have been cursory as with the designs
  • causalSequence content: {'proeth:step': 1, 'proeth:element': 'AI Report Generation', 'proeth:description': "The AI software synthesizes Client W's information and produces a first draft of the report"}; {'proeth:step': 2, 'proeth:element': 'Recognition of Uncertainty', 'proeth:description': "Engineer A, unfamiliar with the tool's accuracy and originality, distrusts the raw output"}; {'proeth:step': 3, 'proeth:element': 'Thorough Report Review', 'proeth:description': 'Engineer A cross-checks key facts against professional journal articles and verifies phrasing via search queries, then personalizes wording'}
  • responsibilityType assessment: direct
  • withinAgentControl assessment: True
RDF JSON-LD
{
  "@context": {
    "proeth": "http://proethica.org/ontology/intermediate#",
    "proeth-case": "http://proethica.org/ontology/case/7#",
    "rdf": "http://www.w3.org/1999/02/22-rdf-syntax-ns#",
    "rdfs": "http://www.w3.org/2000/01/rdf-schema#"
  },
  "@id": "http://proethica.org/ontology/case/7#CausalChain_4",
  "@type": "proeth:CausalChain",
  "proeth:causalLanguage": "Not being familiar with the full functionality of the AI software, including the accuracy and originality of AI-generated text, Engineer A conducted a thorough review of the report, cross-checking key facts against professional journal articles and verifying the phrasing by running search engine queries to ensure the content did not match any existing language.",
  "proeth:causalSequence": [
    {
      "proeth:description": "The AI software synthesizes Client W\u0027s information and produces a first draft of the report",
      "proeth:element": "AI Report Generation",
      "proeth:step": 1
    },
    {
      "proeth:description": "Engineer A, unfamiliar with the tool\u0027s accuracy and originality, distrusts the raw output",
      "proeth:element": "Recognition of Uncertainty",
      "proeth:step": 2
    },
    {
      "proeth:description": "Engineer A cross-checks key facts against professional journal articles and verifies phrasing via search queries, then personalizes wording",
      "proeth:element": "Thorough Report Review",
      "proeth:step": 3
    }
  ],
  "proeth:cause": "AI Report Generation",
  "proeth:counterfactual": "Without the AI-generated draft, this specific verification exercise (fact cross-checking and plagiarism searches) would not have occurred; without Engineer A\u0027s diligence, the review might have been cursory as with the designs",
  "proeth:discoveredInSection": "facts",
  "proeth:effect": "Thorough Report Review",
  "proeth:necessaryFactors": [
    "Existence of the AI-generated first draft",
    "Engineer A\u0027s awareness of unfamiliarity with the AI tool\u0027s accuracy and originality",
    "Engineer A\u0027s technical competence to verify content"
  ],
  "proeth:responsibilityType": "direct",
  "proeth:responsibleAgent": "Engineer A",
  "proeth:sufficientFactors": [
    "AI-generated draft of uncertain accuracy + Engineer A\u0027s professional diligence was enough to prompt cross-checking against professional sources"
  ],
  "proeth:withinAgentControl": true
}

Causal Language: The Client commented that the report read as if written by two different authors but was otherwise satisfactory.

Necessary Factors (NESS):
  • Submission of the draft report to Client W for review
  • Stylistic disparity between the AI-polished introduction and the data-analysis section
  • Nondisclosure of the AI software used to draft the report
Sufficient Factors:
  • A report containing AI-generated polished text alongside Engineer A's own edits, submitted without AI disclosure, was sufficient for the client to perceive two different authors
Counterfactual Test: Had Engineer A disclosed the AI's substantial contribution or harmonized the writing throughout, the client's confusion about authorship would likely have been avoided; per the discussion, 'proactive disclosure could have prevented misunderstandings and strengthened trust'
Responsibility Attribution:

Agent: Engineer A
Type: direct
Within Agent Control: Yes

Causal Sequence:
  1. AI Report Generation
    AI produces an exceptionally polished introductory section
  2. Report Sealing and Submission
    Engineer A submits the sealed draft report without citing AI use
  3. Client Review
    Client W reviews the report and notes uneven quality between sections
  4. Report Inconsistency Observation
    Client W comments that the report reads as if written by two different authors
Field classification (triples vs literals)
Relations (structural triples)
  • cause: Report Sealing and Submission
  • effect: Report Inconsistency Observation
  • responsibleAgent: Engineer A
Literal extractions (kept for synthesis)
  • causalLanguage content: The Client commented that the report read as if written by two different authors but was otherwise satisfactory.
  • necessaryFactors content: Submission of the draft report to Client W for review; Stylistic disparity between the AI-polished introduction and the data-analysis section; Nondisclosure of the AI software used to draft the report
  • sufficientFactors content: A report containing AI-generated polished text alongside Engineer A's own edits, submitted without AI disclosure, was sufficient for the client to perceive two different authors
  • counterfactual content: Had Engineer A disclosed the AI's substantial contribution or harmonized the writing throughout, the client's confusion about authorship would likely have been avoided; per the discussion, 'proactive disclosure could have prevented misunderstandings and strengthened trust'
  • causalSequence content: {'proeth:step': 1, 'proeth:element': 'AI Report Generation', 'proeth:description': 'AI produces an exceptionally polished introductory section'}; {'proeth:step': 2, 'proeth:element': 'Report Sealing and Submission', 'proeth:description': 'Engineer A submits the sealed draft report without citing AI use'}; {'proeth:step': 3, 'proeth:element': 'Client Review', 'proeth:description': 'Client W reviews the report and notes uneven quality between sections'}; {'proeth:step': 4, 'proeth:element': 'Report Inconsistency Observation', 'proeth:description': 'Client W comments that the report reads as if written by two different authors'}
  • responsibilityType assessment: direct
  • withinAgentControl assessment: True
RDF JSON-LD
{
  "@context": {
    "proeth": "http://proethica.org/ontology/intermediate#",
    "proeth-case": "http://proethica.org/ontology/case/7#",
    "rdf": "http://www.w3.org/1999/02/22-rdf-syntax-ns#",
    "rdfs": "http://www.w3.org/2000/01/rdf-schema#"
  },
  "@id": "http://proethica.org/ontology/case/7#CausalChain_5",
  "@type": "proeth:CausalChain",
  "proeth:causalLanguage": "The Client commented that the report read as if written by two different authors but was otherwise satisfactory.",
  "proeth:causalSequence": [
    {
      "proeth:description": "AI produces an exceptionally polished introductory section",
      "proeth:element": "AI Report Generation",
      "proeth:step": 1
    },
    {
      "proeth:description": "Engineer A submits the sealed draft report without citing AI use",
      "proeth:element": "Report Sealing and Submission",
      "proeth:step": 2
    },
    {
      "proeth:description": "Client W reviews the report and notes uneven quality between sections",
      "proeth:element": "Client Review",
      "proeth:step": 3
    },
    {
      "proeth:description": "Client W comments that the report reads as if written by two different authors",
      "proeth:element": "Report Inconsistency Observation",
      "proeth:step": 4
    }
  ],
  "proeth:cause": "Report Sealing and Submission",
  "proeth:counterfactual": "Had Engineer A disclosed the AI\u0027s substantial contribution or harmonized the writing throughout, the client\u0027s confusion about authorship would likely have been avoided; per the discussion, \u0027proactive disclosure could have prevented misunderstandings and strengthened trust\u0027",
  "proeth:discoveredInSection": "facts",
  "proeth:effect": "Report Inconsistency Observation",
  "proeth:necessaryFactors": [
    "Submission of the draft report to Client W for review",
    "Stylistic disparity between the AI-polished introduction and the data-analysis section",
    "Nondisclosure of the AI software used to draft the report"
  ],
  "proeth:responsibilityType": "direct",
  "proeth:responsibleAgent": "Engineer A",
  "proeth:sufficientFactors": [
    "A report containing AI-generated polished text alongside Engineer A\u0027s own edits, submitted without AI disclosure, was sufficient for the client to perceive two different authors"
  ],
  "proeth:withinAgentControl": true
}

Causal Language: Engineer A completed a cursory review of the AI-generated plans and adjusted certain elements to align with site-specific conditions.

Necessary Factors (NESS):
  • Existence of AI-generated preliminary plans and specifications
  • Engineer A's reliance on the AI output without proper oversight
  • Absence of Engineer B's quality assurance function
Sufficient Factors:
  • AI-generated plans combined with Engineer A's choice to rely on them 'without proper oversight' was sufficient to produce only a high-level review
Counterfactual Test: Had Engineer A applied the same thorough scrutiny used for the report — the level of scrutiny required for human-created work — the review would not have been cursory
Responsibility Attribution:

Agent: Engineer A
Type: direct
Within Agent Control: Yes

Causal Sequence:
  1. Confidential Data Input
    Engineer A enters Client W's information into the AI software for design generation
  2. AI Design Generation
    AI-assisted drafting tools generate preliminary plans including basic layouts and technical specifications
  3. Cursory Design Review
    Engineer A completes only a cursory review, adjusting certain elements for site-specific conditions, failing to maintain Responsible Charge
Field classification (triples vs literals)
Relations (structural triples)
  • cause: AI Design Generation
  • effect: Cursory Design Review
  • responsibleAgent: Engineer A
Literal extractions (kept for synthesis)
  • causalLanguage content: Engineer A completed a cursory review of the AI-generated plans and adjusted certain elements to align with site-specific conditions.
  • necessaryFactors content: Existence of AI-generated preliminary plans and specifications; Engineer A's reliance on the AI output without proper oversight; Absence of Engineer B's quality assurance function
  • sufficientFactors content: AI-generated plans combined with Engineer A's choice to rely on them 'without proper oversight' was sufficient to produce only a high-level review
  • counterfactual content: Had Engineer A applied the same thorough scrutiny used for the report — the level of scrutiny required for human-created work — the review would not have been cursory
  • causalSequence content: {'proeth:step': 1, 'proeth:element': 'Confidential Data Input', 'proeth:description': "Engineer A enters Client W's information into the AI software for design generation"}; {'proeth:step': 2, 'proeth:element': 'AI Design Generation', 'proeth:description': 'AI-assisted drafting tools generate preliminary plans including basic layouts and technical specifications'}; {'proeth:step': 3, 'proeth:element': 'Cursory Design Review', 'proeth:description': 'Engineer A completes only a cursory review, adjusting certain elements for site-specific conditions, failing to maintain Responsible Charge'}
  • responsibilityType assessment: direct
  • withinAgentControl assessment: True
RDF JSON-LD
{
  "@context": {
    "proeth": "http://proethica.org/ontology/intermediate#",
    "proeth-case": "http://proethica.org/ontology/case/7#",
    "rdf": "http://www.w3.org/1999/02/22-rdf-syntax-ns#",
    "rdfs": "http://www.w3.org/2000/01/rdf-schema#"
  },
  "@id": "http://proethica.org/ontology/case/7#CausalChain_6",
  "@type": "proeth:CausalChain",
  "proeth:causalLanguage": "Engineer A completed a cursory review of the AI-generated plans and adjusted certain elements to align with site-specific conditions.",
  "proeth:causalSequence": [
    {
      "proeth:description": "Engineer A enters Client W\u0027s information into the AI software for design generation",
      "proeth:element": "Confidential Data Input",
      "proeth:step": 1
    },
    {
      "proeth:description": "AI-assisted drafting tools generate preliminary plans including basic layouts and technical specifications",
      "proeth:element": "AI Design Generation",
      "proeth:step": 2
    },
    {
      "proeth:description": "Engineer A completes only a cursory review, adjusting certain elements for site-specific conditions, failing to maintain Responsible Charge",
      "proeth:element": "Cursory Design Review",
      "proeth:step": 3
    }
  ],
  "proeth:cause": "AI Design Generation",
  "proeth:counterfactual": "Had Engineer A applied the same thorough scrutiny used for the report \u2014 the level of scrutiny required for human-created work \u2014 the review would not have been cursory",
  "proeth:discoveredInSection": "facts",
  "proeth:effect": "Cursory Design Review",
  "proeth:necessaryFactors": [
    "Existence of AI-generated preliminary plans and specifications",
    "Engineer A\u0027s reliance on the AI output without proper oversight",
    "Absence of Engineer B\u0027s quality assurance function"
  ],
  "proeth:responsibilityType": "direct",
  "proeth:responsibleAgent": "Engineer A",
  "proeth:sufficientFactors": [
    "AI-generated plans combined with Engineer A\u0027s choice to rely on them \u0027without proper oversight\u0027 was sufficient to produce only a high-level review"
  ],
  "proeth:withinAgentControl": true
}

Causal Language: Client W, however, noticed several issues with the AI-generated design documents, including misaligned dimensions and an omission of key safety features required by local regulations.

Necessary Factors (NESS):
  • AI-generated errors (misaligned dimensions, omitted safety features) present in the plans
  • Engineer A's failure to detect these errors during the cursory review
  • Client W's own review of the design documents
Sufficient Factors:
  • Undetected AI errors passing through insufficient review into the deliverable, combined with client review, was sufficient for the errors to be discovered by the client; per the discussion, 'the identified errors suggest insufficient review, which could compromise public welfare'
Counterfactual Test: Had Engineer A performed a comprehensive verification, the misaligned dimensions and omitted safety features would likely have been caught before submission, and Client W would not have discovered them
Responsibility Attribution:

Agent: Engineer A
Type: direct
Within Agent Control: Yes

Causal Sequence:
  1. AI Design Generation
    AI tools produce plans containing misaligned dimensions and omitting key safety features
  2. Cursory Design Review
    Engineer A's high-level review fails to detect the errors
  3. Deliverable Submission
    Flawed design documents reach Client W without disclosure of AI involvement
  4. Design Error Discovery
    Client W notices misaligned dimensions and omission of key safety features required by local regulations
Field classification (triples vs literals)
Relations (structural triples)
  • cause: Cursory Design Review
  • effect: Design Error Discovery
  • responsibleAgent: Engineer A
Literal extractions (kept for synthesis)
  • causalLanguage content: Client W, however, noticed several issues with the AI-generated design documents, including misaligned dimensions and an omission of key safety features required by local regulations.
  • necessaryFactors content: AI-generated errors (misaligned dimensions, omitted safety features) present in the plans; Engineer A's failure to detect these errors during the cursory review; Client W's own review of the design documents
  • sufficientFactors content: Undetected AI errors passing through insufficient review into the deliverable, combined with client review, was sufficient for the errors to be discovered by the client; per the discussion, 'the identified errors suggest insufficient review, which could compromise public welfare'
  • counterfactual content: Had Engineer A performed a comprehensive verification, the misaligned dimensions and omitted safety features would likely have been caught before submission, and Client W would not have discovered them
  • causalSequence content: {'proeth:step': 1, 'proeth:element': 'AI Design Generation', 'proeth:description': 'AI tools produce plans containing misaligned dimensions and omitting key safety features'}; {'proeth:step': 2, 'proeth:element': 'Cursory Design Review', 'proeth:description': "Engineer A's high-level review fails to detect the errors"}; {'proeth:step': 3, 'proeth:element': 'Deliverable Submission', 'proeth:description': 'Flawed design documents reach Client W without disclosure of AI involvement'}; {'proeth:step': 4, 'proeth:element': 'Design Error Discovery', 'proeth:description': 'Client W notices misaligned dimensions and omission of key safety features required by local regulations'}
  • responsibilityType assessment: direct
  • withinAgentControl assessment: True
RDF JSON-LD
{
  "@context": {
    "proeth": "http://proethica.org/ontology/intermediate#",
    "proeth-case": "http://proethica.org/ontology/case/7#",
    "rdf": "http://www.w3.org/1999/02/22-rdf-syntax-ns#",
    "rdfs": "http://www.w3.org/2000/01/rdf-schema#"
  },
  "@id": "http://proethica.org/ontology/case/7#CausalChain_7",
  "@type": "proeth:CausalChain",
  "proeth:causalLanguage": "Client W, however, noticed several issues with the AI-generated design documents, including misaligned dimensions and an omission of key safety features required by local regulations.",
  "proeth:causalSequence": [
    {
      "proeth:description": "AI tools produce plans containing misaligned dimensions and omitting key safety features",
      "proeth:element": "AI Design Generation",
      "proeth:step": 1
    },
    {
      "proeth:description": "Engineer A\u0027s high-level review fails to detect the errors",
      "proeth:element": "Cursory Design Review",
      "proeth:step": 2
    },
    {
      "proeth:description": "Flawed design documents reach Client W without disclosure of AI involvement",
      "proeth:element": "Deliverable Submission",
      "proeth:step": 3
    },
    {
      "proeth:description": "Client W notices misaligned dimensions and omission of key safety features required by local regulations",
      "proeth:element": "Design Error Discovery",
      "proeth:step": 4
    }
  ],
  "proeth:cause": "Cursory Design Review",
  "proeth:counterfactual": "Had Engineer A performed a comprehensive verification, the misaligned dimensions and omitted safety features would likely have been caught before submission, and Client W would not have discovered them",
  "proeth:discoveredInSection": "facts",
  "proeth:effect": "Design Error Discovery",
  "proeth:necessaryFactors": [
    "AI-generated errors (misaligned dimensions, omitted safety features) present in the plans",
    "Engineer A\u0027s failure to detect these errors during the cursory review",
    "Client W\u0027s own review of the design documents"
  ],
  "proeth:responsibilityType": "direct",
  "proeth:responsibleAgent": "Engineer A",
  "proeth:sufficientFactors": [
    "Undetected AI errors passing through insufficient review into the deliverable, combined with client review, was sufficient for the errors to be discovered by the client; per the discussion, \u0027the identified errors suggest insufficient review, which could compromise public welfare\u0027"
  ],
  "proeth:withinAgentControl": true
}

Causal Language: Client W raised concerns about the accuracy and reliability of the engineering design and instructed Engineer A to revise the plans, ensuring that all elements satisfied the necessary professional and regulatory standards.

Necessary Factors (NESS):
  • Client W's discovery of misaligned dimensions and omitted safety features
  • Client W's concern about accuracy, reliability, and regulatory compliance
Sufficient Factors:
  • Discovery of safety-related and regulatory deficiencies in the plans was sufficient to trigger the instruction to revise
Counterfactual Test: Without discovering the errors, Client W would have had no basis to raise concerns or instruct a revision; the flawed plans might have proceeded, risking regulatory noncompliance and safety hazards
Responsibility Attribution:

Agent: Client W (issuing the instruction); Engineer A (creating the conditions requiring it)
Type: shared
Within Agent Control: Yes

Causal Sequence:
  1. Design Error Discovery
    Client W discovers misaligned dimensions and omission of key safety features
  2. Client Concern
    Client W raises concerns about the accuracy and reliability of the engineering design
  3. Revision Instruction
    Client W instructs Engineer A to revise the plans to satisfy professional and regulatory standards
Field classification (triples vs literals)
Relations (structural triples)
  • cause: Design Error Discovery
  • effect: Revision Instruction
  • responsibleAgent: Client W (issuing the instruction); Engineer A (creating the conditions requiring it)
Literal extractions (kept for synthesis)
  • causalLanguage content: Client W raised concerns about the accuracy and reliability of the engineering design and instructed Engineer A to revise the plans, ensuring that all elements satisfied the necessary professional and regulatory standards.
  • necessaryFactors content: Client W's discovery of misaligned dimensions and omitted safety features; Client W's concern about accuracy, reliability, and regulatory compliance
  • sufficientFactors content: Discovery of safety-related and regulatory deficiencies in the plans was sufficient to trigger the instruction to revise
  • counterfactual content: Without discovering the errors, Client W would have had no basis to raise concerns or instruct a revision; the flawed plans might have proceeded, risking regulatory noncompliance and safety hazards
  • causalSequence content: {'proeth:step': 1, 'proeth:element': 'Design Error Discovery', 'proeth:description': 'Client W discovers misaligned dimensions and omission of key safety features'}; {'proeth:step': 2, 'proeth:element': 'Client Concern', 'proeth:description': 'Client W raises concerns about the accuracy and reliability of the engineering design'}; {'proeth:step': 3, 'proeth:element': 'Revision Instruction', 'proeth:description': 'Client W instructs Engineer A to revise the plans to satisfy professional and regulatory standards'}
  • responsibilityType assessment: shared
  • withinAgentControl assessment: True
RDF JSON-LD
{
  "@context": {
    "proeth": "http://proethica.org/ontology/intermediate#",
    "proeth-case": "http://proethica.org/ontology/case/7#",
    "rdf": "http://www.w3.org/1999/02/22-rdf-syntax-ns#",
    "rdfs": "http://www.w3.org/2000/01/rdf-schema#"
  },
  "@id": "http://proethica.org/ontology/case/7#CausalChain_8",
  "@type": "proeth:CausalChain",
  "proeth:causalLanguage": "Client W raised concerns about the accuracy and reliability of the engineering design and instructed Engineer A to revise the plans, ensuring that all elements satisfied the necessary professional and regulatory standards.",
  "proeth:causalSequence": [
    {
      "proeth:description": "Client W discovers misaligned dimensions and omission of key safety features",
      "proeth:element": "Design Error Discovery",
      "proeth:step": 1
    },
    {
      "proeth:description": "Client W raises concerns about the accuracy and reliability of the engineering design",
      "proeth:element": "Client Concern",
      "proeth:step": 2
    },
    {
      "proeth:description": "Client W instructs Engineer A to revise the plans to satisfy professional and regulatory standards",
      "proeth:element": "Revision Instruction",
      "proeth:step": 3
    }
  ],
  "proeth:cause": "Design Error Discovery",
  "proeth:counterfactual": "Without discovering the errors, Client W would have had no basis to raise concerns or instruct a revision; the flawed plans might have proceeded, risking regulatory noncompliance and safety hazards",
  "proeth:discoveredInSection": "facts",
  "proeth:effect": "Revision Instruction",
  "proeth:necessaryFactors": [
    "Client W\u0027s discovery of misaligned dimensions and omitted safety features",
    "Client W\u0027s concern about accuracy, reliability, and regulatory compliance"
  ],
  "proeth:responsibilityType": "shared",
  "proeth:responsibleAgent": "Client W (issuing the instruction); Engineer A (creating the conditions requiring it)",
  "proeth:sufficientFactors": [
    "Discovery of safety-related and regulatory deficiencies in the plans was sufficient to trigger the instruction to revise"
  ],
  "proeth:withinAgentControl": true
}
Allen Temporal Relations (10)
Interval algebra relationships with OWL-Time standard properties
From Entity Allen Relation To Entity OWL-Time Property Evidence
Engineer B providing mentorship and quality assurance reviews before
Entity1 is before Entity2
Engineer B's retirement time:intervalBefore
http://www.w3.org/2006/time#intervalBefore
Previously, Engineer A had relied on guidance and quality assurance reviews by their mentor and supe... [more]
Engineer B's retirement before
Entity1 is before Entity2
Engineer A retained by Client W time:intervalBefore
http://www.w3.org/2006/time#intervalBefore
Engineer B recently retired and was no longer available to Engineer A in a work capacity. Faced with... [more]
Engineer A observing the groundwater monitoring site overlaps
Entity1 starts before Entity2 and ends during Entity2
Engineer A retained by Client W to prepare the report and design documents time:intervalOverlaps
http://www.w3.org/2006/time#intervalOverlaps
This work required Engineer A to perform an analysis of groundwater monitoring data from a site Engi... [more]
Engineer A inputting Client W's information into the AI software before
Entity1 is before Entity2
receiving the first draft of the report time:intervalBefore
http://www.w3.org/2006/time#intervalBefore
Engineer A input the information gathered from Client W into the AI software, and, after a few refin... [more]
AI generation of the report draft before
Entity1 is before Entity2
Engineer A's thorough review and cross-checking of the report time:intervalBefore
http://www.w3.org/2006/time#intervalBefore
Not being familiar with the full functionality of the AI software... Engineer A conducted a thorough... [more]
Engineer A's thorough review and personalization of the report before
Entity1 is before Entity2
submission of the sealed draft report to Client W time:intervalBefore
http://www.w3.org/2006/time#intervalBefore
Engineer A also made minor adjustments to some of the wording to personalize the content... and subm... [more]
AI-assisted generation of preliminary design documents before
Entity1 is before Entity2
Engineer A's cursory review and site-specific adjustments time:intervalBefore
http://www.w3.org/2006/time#intervalBefore
relied on the AI-assisted drafting tools to generate a preliminary design of the plans... Engineer A... [more]
submission of the draft report and design documents before
Entity1 is before Entity2
Client W's review time:intervalBefore
http://www.w3.org/2006/time#intervalBefore
When Client W reviewed the draft report, Client W noted that the section analyzing the groundwater m... [more]
Client W's identification of errors in the design documents before
Entity1 is before Entity2
Client W's instruction to Engineer A to revise the plans time:intervalBefore
http://www.w3.org/2006/time#intervalBefore
Client W raised concerns about the accuracy and reliability of the engineering design and instructed... [more]
the case events (report and design document preparation) before
Entity1 is before Entity2
the Discussion section's retrospective ethical analysis time:intervalBefore
http://www.w3.org/2006/time#intervalBefore
The Discussion section retrospectively analyzes these events against NSPE Code provisions and prior ... [more]
About Allen Relations & OWL-Time

Allen's Interval Algebra provides 13 basic temporal relations between intervals. These relations are mapped to OWL-Time standard properties for interoperability with Semantic Web temporal reasoning systems and SPARQL queries.

Each relation includes both a ProEthica custom property and a time:* OWL-Time property for maximum compatibility.