MIT AI Risk Repository · Risk Sub-Category · 11.01.01
Stereotyping social groups
Category: Representational Harms
Description
Stereotyping in an algorithmic system refers to how the system’s outputs reflect “beliefs about the characteristics, attributes, and behaviors of members of certain groups....and about how and why certain attributes go together"
From Sociotechnical Harms of Algorithmic Systems: Scoping a Taxonomy for Harm Reduction (Shelby2023), as extracted by the MIT AI Risk Repository (CC BY 4.0).
Classification
- Causal entity
- AI
- Intent
- Unintentional
- Timing
- Post-deployment
Subdomain definition: Unequal treatment of individuals or groups by AI, often based on race, gender, or other sensitive characteristics, resulting in unfair outcomes and representation of those groups.
Real-world incidents in this subdomain
- DOGE Reportedly Relied on Unvetted ChatGPT Outputs in Canceling National Endowment for the Humanities Grants
- Sora Video Generator Has Reportedly Been Creating Biased Human Representations Across Race, Gender, and Disability
- Meta AI Characters Allegedly Exhibited Racism, Fabricated Identities, and Exploited User Trust
- Alleged AI-Generated Photo Alteration Leads to Inappropriate Modifications in Speaker's Conference Picture
- Algorithmic Bias in French Welfare System Allegedly Discriminates Against Marginalized Groups
- Department for Work and Pensions (DWP) AI Systems Allegedly Discriminate Against Single Mothers