Uncovering Bias in Chest X-Ray Classifiers: A Call for Responsible AI
This AI incident underscores the importance of safe and secure AI, particularly in the medical field. Researchers discovered racial, gender, and socioeconomic biases in chest X-ray classifiers, highlighting the need for trustworthy AI. Join us in promoting harm prevention and strengthening guardrails for AI through Project Cerebellum's AI incident database. This AI incident maps to the Govern function in HISPI Project Cerebellum Trusted AI Model (TAIM). JOIN US
Source
Data from the AI Incident Database (AIID). Cite this incident: https://incidentdatabase.ai/cite/81
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.