Supplementary evidence record for The AI Incident Actionability Gap: Public Evidence and Operational Interpretability in AI Governance

This dataset contains the cleaned supplementary evidence record supporting The AI Incident Actionability Gap: Public Evidence and Operational Interprebility in AI Governance. The workbook documents the structured qualitative coding of 19 publicly documented AI incidents across seven categories of operationally relevant information: (i) system identification, (ii) deployment context, (iii) timeline visibility, (iv) affected population, (v) mitigation visibility, (vi) evidence provenance, and (vii) oversight-route clarity. It includes case-level coding outcomes, supporting rationales, ambiguity notes, principal public evidence sources and evidence-environment metadata. The dataset supports research on AI governance, AI incident reporting and monitoring, post-deployment governance, public evidence, and operational interpretability.