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Final Review

Paper Nº 10

Calibrating Trust in AI. A Co-Designed Checklist for Task-Specific Reliance
30/30
Score
Methodologically the most ambitious (interviews + survey + workshops) with an excellent item-level change log and retained-item rationale, but it stops at two versions, offers no action/decision logic, and uses its N=109 survey almost entirely as confirmation rather than as a driver of change.
Trust Calibration

The Pros

+
Three complementary methods (depth interviews, N=109 survey, group workshops) are deliberately triangulated, and the rationale for each is well argued.
+
Appendix E change log (V1 wording, V2 wording, type, source) with item IDs gives precise, verifiable traceability.
+
Appendix G anonymized per-participant notes tie each revision to a concrete participant statement.
+
Items retained against feedback (Section 4.3) are documented with reasons, a hallmark of honest co-design.
+
Concrete, well-traced design moves (the ML engineer's silently degraded fraud model → versioning item ASF-3) make the changes credible.
+
The survey is honestly scoped as a directional check, and the worked professor case maps items onto genuinely differentiated practice.

The Cons

The co-design cycle stops at V2; only one revision round is produced where peers went to V3.
The tool is purely reflective — no gating, scoring, or stop/revise/delegate/escalate outcomes — so it is less actionable than the decision-rule submissions.
The N=109 survey drives essentially no item revision; it only confirms patterns, a thin payoff for the effort.
Key data (survey CSV, V1/V2 PDFs) live only in an external Google Drive ZIP, so claims like the 87% figure cannot be verified in-paper.
Several V2 items are compound two-sentence prompts (e.g., TC-6), which strains usability.
The "profession-agnostic" claim is tested on a single profession (the professor).
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Final Review · Paper 10The IndexAI Checklists · 2026