Inaccurate Sepsis Predictions by Epic Systems' Algorithms Highlight Need for Responsible AI Governance
August 1, 2021
Investigators at the University of Michigan Hospital found high error rates in Epic Systems' sepsis prediction algorithms, revealing inaccuracies contrary to their published claims. This AI incident maps to the Govern function in HISPI Project Cerebellum Trusted AI Model (TAIM). Ready to help prevent such incidents and ensure safe and secure AI? JOIN US
- Alleged deployer
- university-of-michigan-hospital
- Alleged developer
- epic-systems
- Alleged harmed parties
- sepsis-patients
Source
Data from the AI Incident Database (AIID). Cite this incident: https://incidentdatabase.ai/cite/123
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.