A Blemish in the AI Profession: Examining an Incident for Responsible AI Governance

An incident involving a biased AI system has cast a shadow on the field of artificial intelligence. Here's how Project Cerebellum, HISPI's Trusted AI Model (TAIM) database, can help map, manage, and prevent such occurrences. Through JOIN US, contributors—to learn more about HISPI Project Cerebellum TAIM and become part of the solution for safe and secure AI.

Matched TAIM controls

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

MAP 3.4 — similarity 0.501, rank 1. TAIM detail and related incidents →
MEASURE 1.3 — similarity 0.486, rank 2. TAIM detail and related incidents →
MAP 3.5 — similarity 0.483, rank 3. TAIM detail and related incidents →

Source

Data from the AI Incident Database (AIID). Cite this incident: https://incidentdatabase.ai/cite/43

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.