HISPI Project Cerebellum HISPI PROJECT CEREBELLUM

Chest X-Ray AI Classifier Found to Exhibit Bias Based on Race, Gender, and Socioeconomic Status: A Case for Responsible AI

This case study reveals racial, gender, and socioeconomic bias in chest X-ray classifiers, highlighting the importance of trustworthy AI. By understanding these biases, we can implement guardrails for AI and prevent harm, contributing to safe and secure AI development through Project Cerebellum's AI incident database. Ready to help shape responsible AI? 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.