Aiid
FreeOpen repository cataloging real-world AI harms and failures.
By Tanmay Verma, Founder · Last verified 03 Jul 2026
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Added 6d ago
69/100MonitorIn short
Aiid — Open repository cataloging real-world AI harms and failures. Best for AI safety researchers studying failure patterns across domains, Developers auditing their own AI systems against known harms, Policy analysts drafting AI regulation or guidelines. Free to use.
Is Aiid actually worth it?
LiveSee what real users actually say. We scan live discussions, reviews and complaints across the web and hand you an honest verdict — in under a minute.
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Editorial Verdict
Best for
AI safety researchers studying failure patterns across domainsDevelopers auditing their own AI systems against known harmsPolicy analysts drafting AI regulation or guidelinesJournalists investigating AI-related incidents and trends
Not ideal for
Users seeking a real-time monitoring tool for live AI systemsTeams needing automated incident detection or alertingOrganizations looking for vendor-specific risk assessment reportsAnyone wanting automated analysis or recommendations
If you're serious about AI safety, the AIID is an essential reference. It's free, open, and constantly updated. But don't expect real-time alerts or automated analysis—it's a manual research tool, not a monitoring system.
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Last verified: July 2026
What's new in Aiid
Checked 6 days agoAcross the latest 4 updates: 4 news mentions.
NewsBlog·16 days agoNewest
Notes for the Growing AI Safety Ecosystem: Corporations and Governments
AIID blog post on corporate and government roles in AI safety ecosystem.
NewsBlog·May 5
AI Incident Roundup – February, March, and April 2026
Quarterly roundup of new AI incidents added to the database.
NewsBlog·Feb 2
AI Incident Roundup – November and December 2025 and January 2026
Roundup of incidents from Nov 2025 to Jan 2026.
NewsBlog·Jan 25
Funding the AIID - Part I
First post in a series on AI Incident Database funding.
What independent users actually report about Aiid
We ran a structured research pass across product reviews, community discussions, and post-purchase forum threads to surface the patterns vendors won't publish themselves. Below: the recurring strengths, the hidden costs people mention most, and the cohort that consistently regrets adopting this tool.
5 mentions across 1 source (Lemmy).
50% positive50% critical
Recurring strengths
- +Free, open-access with no registration required.
- +Over 1,500 documented AI incidents as of 2026.
- +Multiple views: table, map, list for exploration.
- +Downloadable full dataset for offline research.
- +API access enables programmatic analysis.
Recurring frustrations
- −Very limited active community discussion or support.
- −No user reviews or ratings to validate tool claims.
- −Lacks built-in collaboration features for teams.
- −No real-time updates; relies on periodic curation.
- −Search could be more powerful with advanced filters.
Patterns worth knowing
Off-topic content dominates available data
Seen on Lemmy
Tool is comprehensive and well-curated based on tool info
Seen on Tool description
Lack of user-generated feedback makes assessment difficult
Seen on Lemmy
Learning curve
beginnerProductive in ~5 minutes
Hidden costs people mention
- • No hidden costs: entirely free and open access
Viability Score
69/100
MonitorHow likely is Aiid to still be operational in 12 months? Based on 4 signals — momentum (how recently it shipped), wrapper dependency, revenue model, and web presence.
momentum
55
funding runway
40
website health
90
wrapper dependency
100
Last calculated: July 2026
How we score →Key Features
- Browse over 1,500 AI incident reports with full metadata
- Table, list, and spatial map views for data exploration
- Filter incidents by entity, taxonomy, and date
- Submit new incident reports via a submission form
- Download the full incident dataset for research
- Random incident discovery feature
- Blog with curated incident roundups and analysis
- Search across all incidents and reports
- API access for programmatic querying
- Entity and taxonomy categorization for structured analysis
- Dark mode and accessibility features
- Subscribe to new incident email notifications
- Flag incidents for review
About Aiid
FreeIntermediateAPI availableWeb · API
The AI Incident Database (AIID) is a free, community-driven public repository that systematically collects, classifies, and shares reports of real-world incidents where AI systems have caused or contributed to harm. With over 1,500 entries—from autonomous vehicle fatalities to algorithmic bias and AI-generated hallucinations in legal filings—it serves as a collective memory for the AI ecosystem. It's built for researchers, developers, product managers, policymakers, and journalists to understand failure patterns, conduct risk assessments, and inform regulation. The platform offers multiple ways to explore incidents: a table view for sorting, a spatial map for geographic trends, entity and taxonomy filters, and a random incident feature. Users can submit new incident reports, which undergo editorial review. The database is open-source, governed by the Responsible AI Collaborative, and funded through donations and grants. All features—including dataset downloads, API access, and the blog with curated roundups—are free and require no registration. Recent additions (as of June 2026) include incidents involving AI-generated hallucinated citations in a KPMG report, the use of Grok to create child sexual abuse material, and alleged price coordination via AI at California gas stations. The database continues to grow, with quarterly roundups and guidance for corporations and governments. Unlike commercial risk assessment tools, the AIID is a historical archive, not a real-time monitor. Its strength lies in breadth and openness, but incident report quality varies with source credibility. It complements active safety testing by providing a reference library of past failures.
Behind the Verdict
The AI Incident Database is a public good in the truest sense. It's one of the few places where you can browse a curated, searchable archive of AI failures—from self-driving car crashes to generative AI hallucinations. We'd reach for this when auditing a system against known failure modes, researching regulatory trends, or teaching AI ethics. The new incident about KPMG's hallucinated citations is a stark reminder that even professional services firms aren't immune. Where it bites: the database is only as good as its submissions. Some incidents are well-sourced news reports; others are single-sourced or lack technical depth. You'll need to cross-check critical findings. Also, there's no real-time alerting—you visit the site or subscribe to the newsletter; you don't get pushed notifications. Compared to vendor-specific risk databases (e.g., those from Credo AI or Fairnow), the AIID is broader but less structured. It's ideal for pattern recognition across domains, not for compliance audits against a specific regulation. The API and full dataset download make it useful for researchers who want to run their own analyses. In practice, we'd use it alongside active testing tools. It won't catch the next vulnerability, but it will remind you that every failure has happened before.
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Use Cases
- Research historical AI failures to identify recurring patterns and risks.
- Support policy submissions with empirical evidence from documented incidents.
- Integrate incident data into AI safety checklists for model deployment.
- Use the dataset for academic research on AI harm taxonomy.
- Educate teams on the real-world consequences of AI system failures.
Limitations
- Incident reports are sourced from public records and submissions, which may introduce reporting bias or lag.
- The database relies on community contributions for completeness, so not all incidents may be captured.
- API access is available but documentation is minimal.
Integrations
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