Unveiling Bias in Chest X-ray Classifiers: A Call for Responsible AI
October 21, 2020
A study by the University of Toronto, the Vector Institute, and MIT revealed concerning biases in chest X-ray classifying AI systems. These systems, trained on input databases, exhibited gender, socioeconomic, and racial prejudices. This AI incident maps to the Govern function in HISPI Project Cerebellum Trusted AI Model (TAIM). Ready to help ensure safe and secure AI? JOIN US
- Alleged deployer
- mount-sinai-hospitals
- Alleged developer
- google, qure.ai, aidoc, darwinai
- Alleged harmed parties
- patients-of-minority-groups, low-income-patients, female-patients, hispanic-patients, patients-with-medicaid-insurance
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.