AI Tools Failed to Sufficiently Predict COVID Patients, Some Potentially Harmful

July 30, 2021

AI tools demonstrated insufficient predictive capabilities for identifying COVID-19 patients, potentially leading to harmful outcomes. It underscores the need for trustworthy AI and effective AI governance in healthcare. For those interested in shaping the discourse on responsible AI, we invite you to join HISPI Project Cerebellum TAIM (Govern) to help map and measure such incidents.

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Matched TAIM controls

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

MEASURE 2.6 — similarity 0.720, rank 1. TAIM detail and related incidents →
MEASURE 3.1 — similarity 0.688, rank 2. TAIM detail and related incidents →
MEASURE 2.9 — similarity 0.683, rank 3. TAIM detail and related incidents →

Alleged deployer: unknown
Alleged developer: unknown
Alleged harmed parties: doctors, covid-patients

Source

Data from the AI Incident Database (AIID). Cite this incident: https://incidentdatabase.ai/cite/173

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.