Addressing Popularity Bias in AI-Powered Media Recommendations

Explore the impact of popularity bias in collaborative filtering-based multimedia recommender systems, a common AI application. This bias can lead to an unbalanced representation of content, potentially limiting user exposure and reinforcing stereotypes. To ensure safe and secure AI recommendations, it's crucial to develop responsible AI governance strategies that mitigate this issue.

Matched TAIM controls

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

MEASURE 3.3 — similarity 0.528, rank 1. TAIM detail and related incidents →
MAP 1.2 — similarity 0.518, rank 2. TAIM detail and related incidents →
MEASURE 2.11 — similarity 0.508, rank 3. TAIM detail and related incidents →

Source

Data from the AI Incident Database (AIID). Cite this incident: https://incidentdatabase.ai/cite/168

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.