Systemic Altruism
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Justice & public goodsPreliminary editorial review

Responsible AI Collaborative (AI Incident Database)

Responsible AI Collaborative Inc. advances the AI Incident Database, a shared incident-learning resource with an inspected software repository. Software permissions, selected database collections and third-party reports have different terms and evidentiary limits.

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Systemic Altruism practice potential: 68–78/100
90% of weighted axes reviewed. A mechanism hypothesis, not proven effectiveness or a donation recommendation.

Who this work enables

Tools & support for charities: The AI Incident Database’s reusable application, taxonomies and curation workflow can help institutions learn from reported failures without independently rebuilding the whole incident-discovery infrastructure.

Open-source evidence · license checked

License checked 2026-10-27. This is not a security audit, proof of current support or effectiveness, or a promise of free hosting. A sponsor or steward is not necessarily the sole developer. Unchecked profiles are not evidence of closed code. No extra score points are added.

Humanity’s Checklist source match

Coordination — editorial, partial alignment.

Shared incident records and taxonomies support collective learning across otherwise separate organizations. This is a partial Coordination connection, not proof of representative incident coverage, harm prevention or checklist adoption.

Explicit checklist adoption: not verified. A mechanism match is not an affiliation, endorsement, evidence of effectiveness or a scoring bonus. Read the practice.

Focus classification: 2026-10-27. Based on the organization’s dated official sources below; earlier axis-review dates are unchanged.

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The proposed pathway

Make reported AI failures available for shared institutional learning

Six dimensions, with rationale

Structural depth

3 / 4

Shared incident memory and analytical tooling can change how institutions learn from AI failures rather than investigate every event in isolation.

Institutional reach

3 / 4

A common incident resource can enable researchers, civil society and public institutions across sectors; downstream risk reduction was not independently measured.

Durability

2 / 4

Records and reusable code can persist, but curation, corrections, source access and operational support require continued work.

Scalability

4 / 4

Common software and taxonomies can support many users, while reporting bias, verification, language coverage and contextual interpretation limit generalization.

Funding additionality

Unreviewed

No current marginal curation or maintenance bottleneck, alternative support from collaborators or causal donation counterfactual was audited.

Community agency

Unreviewed

Contributor participation and public submissions do not establish binding rights for people harmed by AI over nonprofit budgets, classifications, corrections or database priorities.

Evidence and selection

Organization reported; current primary mechanism sources inspected; named repository and license file checked

Illustrative inclusion to examine this mechanism. Inclusion is not an external recommendation or a verified comparative ranking.

A testable next question

Do public-interest teams using a specified AIID learning workflow identify and mitigate independently verified failure modes better than comparable teams, accounting for reporting bias, uncertain allegations, existing expertise and the cost of verification?

Review confidence: Moderate for documented design; low for systemic attribution. Responsible AI Collaborative Inc.’s AI Incident Database program, not every contributor, DSRI / UL Research Institutes activity or the whole Partnership on AI. The initiative is not an extra independent charity beyond its named nonprofit.

What remains uncertain

The organization’s own 2024 Form 990-PF identifies Responsible AI Collaborative Inc., EIN 88-1046583; the fixed registry also classifies it as a private operating foundation, not a public charity. A dated filing corroborates identity, not current eligibility, solvency or governance. The about page credits DSRI support and many independent contributors; RAIC is not sole author of every activity. LICENSE.txt grants Apache-2.0 for software, while terms describe CC BY-SA for specified database collections and preserve separate third-party-content rights. No broad right to republish news or reports is inferred. Incident reports may contain allegations and selection bias; counts are not prevalence estimates, adjudicated facts or proof that using the database prevents harm. Hosted/API terms, moderation, privacy and dependencies need separate review.

Sources to inspect

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Axis review: 2026-10-27 · weighting method v0.2 (2026-10-26) · Method, conflicts and coverage