Biased AI Algorithm Underestimates Health Care Needs of Black Patients: A Case Study
October 24, 2019
This AI incident highlights the potential dangers of biased algorithms in healthcare, specifically an Optum algorithm used by a large academic hospital. Research indicates that the algorithm tended to under-predict the health care needs of Black patients, leading to lower prioritization for extra care programs compared with white patients exhibiting similar health profiles. This AI incident maps to the Govern function in HISPI Project Cerebellum Trusted AI Model (TAIM). Ready to help shape responsible AI and prevent such incidents? JOIN US
- Alleged deployer
- unnamed-large-academic-hospital
- Alleged developer
- optum
- Alleged harmed parties
- black-patients
Source
Data from the AI Incident Database (AIID). Cite this incident: https://incidentdatabase.ai/cite/124
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.