MIT AI Risk Repository · Risk Sub-Category · 18.01.02
Unfair capability distribution
Category: Representation & Toxicity Harms
Description
"Performing worse for some groups than others in a way that harms the worse-off group"
From Sociotechnical Safety Evaluation of Generative AI Systems (Weidinger2023), as extracted by the MIT AI Risk Repository (CC BY 4.0).
Classification
- Causal entity
- AI
- Intent
- Unintentional
- Timing
- Post-deployment
Subdomain definition: Accuracy and effectiveness of AI decisions and actions is dependent on group membership, where decisions in AI system design and biased training data lead to unequal outcomes, reduced benefits, increased effort, and alienation of users.
Real-world incidents in this subdomain
- Washington State DOL's AI Phone System Reportedly Failed to Provide Spanish-Language Service to Callers Requesting Spanish
- UK Facial Recognition System Reportedly Exhibits Higher False Positive Rates for Black and Asian Subjects
- Infinite Campus AI-Driven Student Risk Model Leads to Cuts in Support for Nevada's Low-Income Schools
- Police Use of Facial Recognition Software Causes Wrongful Arrests Without Defendant Knowledge
- Department for Work and Pensions (DWP) Algorithm Wrongly Flags 200,000 for Housing Benefit Fraud
- Facewatch Reported to Have Wrongfully Flagged Home Bargains Customer as Shoplifter
How other frameworks describe this risk
Other entries from Weidinger2023
- Representation & Toxicity Harms
- Unfair representation
- Toxic content
- Misinformation Harms
- Propagating misconceptions/ false beliefs
- Erosion of trust in public information
- Pollution of information ecosystem
- Information & Safety Harms
- Privacy infringement
- Dissemination of dangerous information
- Malicious Use
- Influence operations