The Importance of Responsible AI: Understanding the Impact of Biases in AI Systems

Explore a recent incident involving an AI system that displayed biased results based on names, highlighting the need for trustworthy and safe AI practices. This article underscores the significance of AI governance and harm prevention as we strive to develop responsible AI systems.

Matched TAIM controls

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

MAP 1.6 — similarity 0.431, rank 1. TAIM detail and related incidents →
MEASURE 2.10 — similarity 0.430, rank 2. TAIM detail and related incidents →
MEASURE 2.2 — similarity 0.430, rank 3. TAIM detail and related incidents →

Source

Data from the AI Incident Database (AIID). Cite this incident: https://incidentdatabase.ai/cite/19

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.