AI Discharge: How Responsible AI Could Have Prevented Unfair Termination

An unfortunate incident occurred when a man was dismissed by an algorithmic decision-making system, raising questions about the need for safe and secure AI. The system used to evaluate his performance made mistakes that could have been avoided with responsible AI practices. This underscores the importance of trustworthy AI governance and the role of HISPI Project Cerebellum TAIM in ensuring incident prevention and promoting guardrails for AI.

Matched TAIM controls

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

MEASURE 2.6 — similarity 0.605, rank 1. TAIM detail and related incidents →
MEASURE 2.5 — similarity 0.584, rank 2. TAIM detail and related incidents →
MANAGE 2.4 — similarity 0.570, rank 3. TAIM detail and related incidents →

Source

Data from the AI Incident Database (AIID). Cite this incident: https://incidentdatabase.ai/cite/35

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.