Towards Trustworthy AI Assistants in Safety-Critical Engineering: A Co-Designed Trust Calibration Checklist · Final Review · Score 29/30Towards Trustworthy AI Assistants in Safety-Critical Engineering: A Co-Designed Trust Calibration Checklist · Final Review · Score 29/30Towards Trustworthy AI Assistants in Safety-Critical Engineering: A Co-Designed Trust Calibration Checklist · Final Review · Score 29/30Towards Trustworthy AI Assistants in Safety-Critical Engineering: A Co-Designed Trust Calibration Checklist · Final Review · Score 29/30Towards Trustworthy AI Assistants in Safety-Critical Engineering: A Co-Designed Trust Calibration Checklist · Final Review · Score 29/30Towards Trustworthy AI Assistants in Safety-Critical Engineering: A Co-Designed Trust Calibration Checklist · Final Review · Score 29/30
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Two-tier reviewer design (5 non-experts for clarity, 5 ADAS practitioners from JLR for industrial realism) is well sequenced and justified.
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Section 0 task-criticality gate plus Table 2 fulfillment thresholds turns the checklist into a decision instrument rather than a descriptive artifact.
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EARS operationalization (S1-Q1, S2-Q5, etc.) concretely shows abstract trust items becoming verifiable requirements.
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Appendix E granular V2 → V3 changelog and Appendix B meeting minutes give an auditable evidence trail.
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The applied ADAS use case walks every section against a single realistic requirement-generation tool.
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Section 3 (V3) items 7 and 8 are identical, and Section 1 items 4 and 5 are near-duplicates, which is ironic given the paper's stated goal of removing redundancy.
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Table 2 thresholds (100/80/60/40/20%) are asserted with no grounding or rationale.
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Traceability is asymmetric: V2 → V3 has a granular changelog, but V1 → V2 is only narrative.
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Most design decisions are attributed generically to "the participants" rather than specific experts.
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Outcome logic is inconsistent: the abstract frames a binary Go/No-Go, while the text and Table 2 imply three outcomes (granted / conditionally granted / denied).
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The use case is illustrative only, with no real tool output or a worked example where an item failure flips the Go/No-Go result.