MIT AI Risk Repository · Risk Category · 01.03.00
Type 3: Worse than expected
Description
AI intended to have a large societal impact can turn out harmful by mistake, such as a popular product that creates problems and partially solves them only for its users.
From TASRA: a Taxonomy and Analysis of Societal-Scale Risks from AI (Critch2023), 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 Critch2023
- Type 1: Diffusion of responsibility
- Type 1: Diffusion of responsibility
- Type 1: Diffusion of responsibility
- Type 1: Diffusion of responsibility
- Type 2: Bigger than expected
- Type 2: Bigger than expected
- Type 3: Worse than expected
- Type 3: Worse than expected
- Type 3: Worse than expected
- Type 3: Worse than expected
- Type 3: Worse than expected
- Type 3: Worse than expected