HISPI Project Cerebellum HISPI PROJECT CEREBELLUM

Chest X-ray Classifier Bias: Unveiling Racial, Gender, and Socioeconomic Disparities in AI

Investigating racial, gender, and socioeconomic bias within chest X-ray classifiers, this research emphasizes the importance of responsible AI governance. By promoting trustworthy AI and safe and secure systems, we can mitigate such disparities. Want to join our efforts in harm prevention? 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.