Algorithmic Bias: Unveiling the Impact on Kidney Transplants for Black Patients

Recent research has uncovered a concerning incident involving an algorithm that has inadvertently affected the allocation of kidney transplants to African American patients. This bias, while not intentional, highlights the urgent need for responsible AI governance and guardrails to prevent such harm.

Matched TAIM controls

Suggested mapping from embedding similarity (not a formal assessment). Browse all TAIM controls

MAP 1.6 — similarity 0.493, rank 1. TAIM detail and related incidents →
MEASURE 2.10 — similarity 0.484, rank 2. TAIM detail and related incidents →
MEASURE 2.11 — similarity 0.480, rank 3. TAIM detail and related incidents →

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.