Unveiling Bias in Chest X-Ray AI Classifiers: Ensuring Trustworthy AI through Project Cerebellum
Recent research reveals racial, gender, and socioeconomic biases in AI chest X-ray classifiers. It's crucial to address these disparities for safe and secure AI development. This AI incident maps to the Govern function in HISPI Project Cerebellum Trusted AI Model (TAIM). Harm prevention relies on robust guardrails for AI.Ready to contribute to responsible AI governance? 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.