Risks vs Incidents: Cause Mismatch
Comparing full causal taxonomy distributions between risks (academic predictions) and incidents (real-world outcomes) per subdomain. Percentages use coded-only denominators, excluding “Not coded” records for cleaner comparison.
Discrimination & Toxicity
1.2 Toxic content
76 coded risks · 90 coded incidents
1.3 Unequal performance
17 coded risks · 34 coded incidents
1.1 Discrimination
82 coded risks · 118 coded incidents
Privacy & Security
2.2 AI security vulnerabilities
111 coded risks · 21 coded incidents
2.1 Loss of privacy
77 coded risks · 88 coded incidents
Misinformation
3.1 False information
53 coded risks · 187 coded incidents
Malicious Actors & Misuse
4.1 Disinformation & influence
82 coded risks · 135 coded incidents
4.2 AI weapons & cyberattacks
80 coded risks · 13 coded incidents
4.3 AI fraud & scams
77 coded risks · 394 coded incidents
Human-Computer Interaction
5.1 Overreliance & unsafe use
60 coded risks · 36 coded incidents
Socioeconomic & Environmental
6.1 Power centralization
51 coded risks · 6 coded incidents
6.2 Inequality & unemployment
54 coded risks · 6 coded incidents
6.3 Devaluation of human creativity
31 coded risks · 5 coded incidents
AI System Safety, Failures & Limitations
7.3 Capability & robustness
123 coded risks · 302 coded incidents
7.4 Transparency & interpretability
41 coded risks · 5 coded incidents
Key Takeaways
- 1.1.2 Toxic content has the largest reliable mismatch (Unintentional +52.6pp in intent).
- 2.Incidents are systematically more AI system than risks predict in 80% of subdomains (median gap: +28.8pp).
- 3.Incidents are systematically more Intentional than risks predict in 87% of subdomains (median gap: +14.4pp).
- 4.9 subdomains with fewer than 5 incidents are confidence-weighted. Percentages use coded-only denominators (excluding "Not coded" records).