Work

Public AI incident documentation rarely preserves exposure denominators, follow-up windows, or closure bases: data, coding manual, and analysis scripts (v25)

Year: 2026

Type: preprint

Abstract: This archive accompanies a manuscript proposing a candidate minimum longitudinal record-and-follow-up layer for AI incident governance. The public-source documentation audit comprises 100 publicly retrievable records grouped into 91 deduplication clusters, drawn from three source families (60 AI Incident Database snapshot records; 20 OECD AI Incidents Monitor records; 20 direct official, regulatory, or company/public-report records). The archive contains: (1) the case-level coding table with record-level and cluster-level summaries and descriptive Wilson intervals; (2) the source registry with provenance and recheck state; (3) the frozen coding manual; (4) the machine-readable schema template (JSON Schema); (5) analysis scripts reproducing all summaries; and (6) a second-coder reliability worksheet and protocol. The audit describes public-documentation recoverability only; it is not an incidence estimate, a causal analysis, or a protocol validation.

Source: Zenodo (CERN European Organization for Nuclear Research)

DOI: 10.5281/zenodo.22132653

Author: Su, Shuqiong

Institutions: Guangxi Medical University, Tumor Hospital of Guangxi Medical University

Language: English

Topic: Ethics and Social Impacts of AI

Subfield: Safety Research

Field: Social Sciences

Domain: Social Sciences

SDG: Peace, justice, and strong institutions

Open Access status: green

OpenAlex ID: W7204449097

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