Pith. sign in

REVIEW 2 cited by

Preventing Repeated Real World AI Failures by Cataloging Incidents: The AI Incident Database

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2011.08512 v1 pith:DC7RK4VJ submitted 2020-11-17 cs.CY cs.SE

classification cs.CYcs.SE
keywords incidentintelligentrealworlddatabasefailuressystemscollection
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

Mature industrial sectors (e.g., aviation) collect their real world failures in incident databases to inform safety improvements. Intelligent systems currently cause real world harms without a collective memory of their failings. As a result, companies repeatedly make the same mistakes in the design, development, and deployment of intelligent systems. A collection of intelligent system failures experienced in the real world (i.e., incidents) is needed to ensure intelligent systems benefit people and society. The AI Incident Database is an incident collection initiated by an industrial/non-profit cooperative to enable AI incident avoidance and mitigation. The database supports a variety of research and development use cases with faceted and full text search on more than 1,000 incident reports archived to date.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Stakeholder Participation for Responsible AI Development: Disconnects Between Guidance and Current Practice

    cs.SE 2025-06 conditional novelty 5.0 of 10

    Industry stakeholder involvement in AI development is driven by customer value and compliance, and currently contributes little to the responsible AI benefits that guidance documents promise.

  2. Unsafe at any AUC: Unlearned Lessons from Sociotechnical Disasters for Responsible AI

    cs.CY 2026-07 accept novelty 4.0 of 10

    AI safety is a systems-governance problem: six recurring organizational failure patterns from past disasters remain unlearned in AI development, so component-level fixes like benchmarks and alignment cannot deliver safety.

Pith tools