MIT AI Risk Repository · Risk Category · 24.01.00
Capability failures
Description
"One reason AI systems fail is because they lack the capability or skill needed to do what they are asked to do."
From The Ethics of Advanced AI Assistants (Gabriel2024), 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
- Other
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 Gabriel2024
- Lack of capability for task
- Difficult to develop metrics for evaluating benefits or harms caused by AI assistants
- Safe exploration problem with widely deployed AI assistants
- Goal-related failures
- Misaligned consequentialist reasoning
- Specification gaming
- Goal misgeneralisation
- Deceptive alignment
- Malicious Uses
- Offensive Cyber Operations (General)
- AI-Powered Spear-Phishing at Scale
- AI-Assisted Software Vulnerability Discovery