Addressing Popularity Bias in Collaborative Filtering for Trustworthy AI: A Case Study
March 1, 2022
This AI incident highlights the potential pitfalls of collaborative filtering algorithms, prone to popularity bias. The overrepresentation of popular items can distort recommendations and impact user experience. By addressing such issues, we strive towards responsible AI governance and safer, more secure recommendations in Project Cerebellum's AI Incident Database. Ready to help shape responsible AI? JOIN US This AI incident maps to the Govern function in HISPI Project Cerebellum Trusted AI Model (TAIM).
- Alleged deployer
- facebook, linkedin, youtube, twitter, netflix
- Alleged developer
- facebook, linkedin, youtube, twitter, netflix
- Alleged harmed parties
- facebook-users, linkedin-users, youtube-users, twitter-users, netflix-users
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.