Racial, Gender, and Intersectional Bias in Unsupervised Image Generation Models: iGPT and SimCLR
June 17, 2020
Uncovering racial, gender, and intersectional biases in popular unsupervised image generation models such as iGPT and SimCLR. These models, trained using Internet data, inadvertently produced stereotypical depictions. This AI incident maps to the Govern function in HISPI Project Cerebellum Trusted AI Model (TAIM). JOIN US: Contribute to harm prevention by implementing guardrails for AI.
- Alleged deployer
- openai, google
- Alleged developer
- openai, google
- Alleged harmed parties
- gender-minority-groups, racial-minority-groups, underrepresented-groups-in-training-data
Source
Data from the AI Incident Database (AIID). Cite this incident: https://incidentdatabase.ai/cite/367
Data source
Incident data is from the AI Incident Database (AIID).
When citing the database as a whole, please use:
McGregor, S. (2021) Preventing Repeated Real World AI Failures by Cataloging Incidents: The AI Incident Database. In Proceedings of the Thirty-Third Annual Conference on Innovative Applications of Artificial Intelligence (IAAI-21). Virtual Conference.
Pre-print on arXiv · Database snapshots & citation guide
We use weekly snapshots of the AIID for stable reference. For the official suggested citation of a specific incident, use the “Cite this incident” link on each incident page.