Thousands Mistakenly Accused by Biased AI System: A Case for Responsible AI Governance

Recent incidents highlight the potential harm of discriminatory algorithms, as thousands of families were falsely accused of fraud. This underscores the need for robust safeguards in AI systems to prevent such occurrences and ensure fairness. HISPI Project Cerebellum TAIM offers tools to Map, Measure, and Manage trustworthy AI models. Contribute—JOIN US—to learn more.

Matched TAIM controls

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

MEASURE 2.10 — similarity 0.516, rank 1. TAIM detail and related incidents →
MEASURE 2.11 — similarity 0.505, rank 2. TAIM detail and related incidents →
GOVERN 3.1 — similarity 0.498, rank 3. TAIM detail and related incidents →

Source

Data from the AI Incident Database (AIID). Cite this incident: https://incidentdatabase.ai/cite/101

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.