Algorithm Bias: The Case of Kidney Transplant Disparities - Ensuring Trustworthy AI through Project Cerebellum
Exploring the unjust algorithmic bias that led to fewer kidney transplants for Black patients, this article highlights the importance of responsible AI governance and the need for safe and secure AI systems. By joining our effort at Project Cerebellum, you can help us prevent such incidents through the implementation of guardrails for AI. This AI incident maps to the Govern function in HISPI Project Cerebellum Trusted AI Model (TAIM). JOIN US
Source
Data from the AI Incident Database (AIID). Cite this incident: https://incidentdatabase.ai/cite/79
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.