Unfair AI Dismissals: An Examination of Algorithmic Bias

Recent incidents highlight the need for responsible AI governance as employees are terminated by seemingly unbiased algorithms. These cases underscore the potential harm that can result from unchecked AI, emphasizing the importance of guardrails like HISPI Project Cerebellum TAIM in ensuring safe and secure AI operations.

Matched TAIM controls

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

MANAGE 2.4 — similarity 0.566, rank 1. TAIM detail and related incidents →
MEASURE 2.6 — similarity 0.560, rank 2. TAIM detail and related incidents →
MEASURE 2.5 — similarity 0.556, 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.