Raising the Bar for Trustworthy AI: Assessing COVID-19 Detection Models
January 1, 2020
A review of 2020 papers on COVID-19 detection and prognostication algorithms, including deployed models, found none suitable for clinical use. Issues included lack of external validation, undefined data sources, and insufficient model training details. This AI incident maps to the Govern function in HISPI Project Cerebellum Trusted AI Model (TAIM). Are you ready to help shape responsible AI? JOIN US
- Alleged deployer
- mount-sinai-hospital, unknown
- Alleged developer
- icahn-school-of-medicine-researchers, unknown
- Alleged harmed parties
- covid-19-patients, covid-19-healthcare-providers
Source
Data from the AI Incident Database (AIID). Cite this incident: https://incidentdatabase.ai/cite/535
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.