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Home/Incidents/Incidents Over Time

These visualizations are experimental and still being refined. Designs and data presentation may change.

Incidents Over Time

How are incidents distributed over time? Color by risk subdomain or causal taxonomy dimension (entity, intent, timing), with a domain filter for cross-cutting drill-down.

How to read this

Stacked bars show incident counts by year. Use Color by to switch between subdomain and causal taxonomy breakdowns. Use the Domain filter to focus on specific domains or subdomains. Toggle between count (absolute) and proportion (relative share). Click any bar segment to browse matching incidents.

Key Takeaways

1.1,346 incidents classified across 24 risk subdomains from 2012 to present.
2.4.3 Fraud, scams, and targeted manipulation has the most incidents at 352 (26%).
3.Use the domain filter to drill down into subdomain distributions within each of the 7 risk domains.
4.Most domains show increasing concentration in 1-2 dominant subdomains over time.

Incident data from the AI Incident Database, classified using the MIT AI Risk Repository taxonomy. Incidents prior to 2012 not shown.

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1. Discrimination & Toxicity:

1.1 Unfair discrimination and misrepresentation

1.2 Exposure to toxic content

1.3 Unequal performance across groups

2. Privacy & Security:

2.1 Compromise of privacy by leaking or correctly inferring sensitive information

2.2 AI system security vulnerabilities and attacks

3. Misinformation:

3.1 False or misleading information

3.2 Pollution of information ecosystem and loss of consensus reality

4. Malicious Actors & Misuse:

4.1 Disinformation, surveillance, and influence at scale

4.2 Cyberattacks, weapon development or use, and mass harm

4.3 Fraud, scams, and targeted manipulation

5. Human-Computer Interaction:

5.1 Overreliance and unsafe use

5.2 Loss of human agency and autonomy

6. Socioeconomic & Environmental:

6.1 Power centralization and unfair distribution of benefits

6.2 Increased inequality and decline in employment quality

6.3 Economic and cultural devaluation of human effort

6.4 Competitive dynamics

6.5 Governance failure

6.6 Environmental harm

7. AI System Safety, Failures & Limitations:

7.1 AI pursuing its own goals in conflict with human goals or values

7.2 AI possessing dangerous capabilities

7.3 Lack of capability or robustness

7.4 Lack of transparency or interpretability

7.5 AI welfare and rights

7.6 Multi-agent risks

Source: MIT AI Risk Repository