MIT AI Risk Repository · Risk Category · 56.10.00
Poor performance of a model used for its intended purpose, for example leading to biased decisions
Description
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From Future Risks of Frontier AI (GOS2023), as extracted by the MIT AI Risk Repository (CC BY 4.0).
Classification
- Subdomain
- 7.3 Lack of capability or robustness
- Causal entity
- AI
- Intent
- Unintentional
- Timing
- Post-deployment
Subdomain definition: AI systems that fail to perform reliably or effectively under varying conditions, exposing them to errors and failures that can have significant consequences, especially in critical applications or areas that require moral reasoning.
Real-world incidents in this subdomain
- Purported AI Name-Reading System Reportedly Skipped and Misannounced Graduates at Arizona's Glendale Community College Commencement
- PocketOS Production Database Was Reportedly Deleted by Cursor AI Agent Running Claude Opus 4.6
- Baidu Apollo Go Robotaxis Stopped in Traffic During Reported System Failure in Wuhan, Stranding Some Passengers
- Purportedly AI-Enabled Targeting System Was Reportedly Implicated in Deadly U.S. Strike on Iranian Primary School
- Claude Code Agent Reportedly Deleted DataTalks.Club Production Infrastructure, Database, and Snapshots via Terraform
- Purportedly AI-Generated Sepsis Alert Reportedly Prompted Potentially Inappropriate IV Fluid Administration for a Dialysis Patient, Averted by Clinician Intervention
How other frameworks describe this risk
Other entries from GOS2023
- Discrimination
- Inequality
- Environmental impacts
- Amplification of biases
- Harmful responses
- Lack of transparency and interpretability
- Intellectual property rights
- Providing new capabilities to a malicious actor
- Misapplication by a non-malicious actor
- Unintended outcomes from interactions with other AI systems
- Impacts resulting from interactions with external societal, political, and economic systems
- Loss of human control and oversight, with an autonomous model then taking harmful actions