HISPI Project Cerebellum HISPI PROJECT CEREBELLUM

Unveiling Racial, Gender, and Socioeconomic Bias in Chest X-Ray AI Classifiers: A Call for Responsible AI

This AI incident highlights the urgent need for trustworthy, unbiased AI in healthcare. Researchers discovered racial, gender, and socioeconomic biases in chest X-ray classifiers. By joining us at Project Cerebellum, you can help establish guardrails for AI, ensuring safe and secure development of such critical systems. This AI incident maps to the Govern function in HISPI Project Cerebellum Trusted AI Model (TAIM).

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.