Biased bots: Artificial-intelligence systems echo human prejudices

Artificial Intelligence (AI) systems, designed to learn from human interactions, can unintentionally mirror inherent biases. This incident highlights the importance of responsible AI governance and safe & secure AI practices, ensuring that these systems do not perpetuate prejudices in decision-making processes.

Matched TAIM controls

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

MAP 1.6 — similarity 0.619, rank 1. TAIM detail and related incidents →
MEASURE 2.5 — similarity 0.602, rank 2. TAIM detail and related incidents →
MEASURE 2.10 — similarity 0.597, rank 3. TAIM detail and related incidents →

AI governance case studies

For forensic AI governance failure analysis (TAIMScore™ case studies), browse Human Signal’s Failure Files™.

Source

Data from the AI Incident Database (AIID). Cite this incident: https://incidentdatabase.ai/cite/59

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.