HISPI Project Cerebellum HISPI PROJECT CEREBELLUM

Examining Racial, Gender, and Socioeconomic Bias in Chest X-ray AI Classifiers - A Step Towards Responsible Healthcare AI

This AI incident underscores the importance of trustworthy healthcare AI. Researchers identified biases in chest X-ray classifiers based on patient demographics, emphasizing the need for robust governance and harm prevention mechanisms. Join us in shaping safe and secure AI with Project Cerebellum - JOIN US. 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.