The Case of Amazonfail: Responsible AI Incidents Require Action

In the recent Amazonfail incident, a recruitment tool reportedly discriminated against candidates based on gender, raising concerns about AI governance and harm prevention. This underscores the urgent need for safe and secure AI and robust trustworthy AI models within organizations like Amazon. HISPI Project Cerebellum TAIM provides tools to manage such incidents and encourages collaboration—JOIN US—to learn more.

Matched TAIM controls

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

GOVERN 1.7 — similarity 0.481, rank 1. TAIM detail and related incidents →
GOVERN 6.1 — similarity 0.463, rank 2. TAIM detail and related incidents →
MEASURE 3.3 — similarity 0.456, rank 3. TAIM detail and related incidents →

Source

Data from the AI Incident Database (AIID). Cite this incident: https://incidentdatabase.ai/cite/15

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.