Journal article
The multisided complexity of fairness in recommender systems
The AI magazine, v 43(2), pp 164-176
01 Jun 2022
Abstract
Recommender systems are poised at the interface between stakeholders: for example, job applicants and employers in the case of recommendations of employment listings, or artists and listeners in the case of music recommendation. In such multisided platforms, recommender systems play a key role in enabling discovery of products and information at large scales. However, as they have become more and more pervasive in society, the equitable distribution of their benefits and harms have been increasingly under scrutiny, as is the case with machine learning generally. While recommender systems can exhibit many of the biases encountered in other machine learning settings, the intersection of personalization and multisidedness makes the question of fairness in recommender systems manifest itself quite differently. In this article, we discuss recent work in the area of multisided fairness in recommendation, starting with a brief introduction to core ideas in algorithmic fairness and multistakeholder recommendation. We describe techniques for measuring fairness and algorithmic approaches for enhancing fairness in recommendation outputs. We also discuss feedback and popularity effects that can lead to unfair recommendation outcomes. Finally, we introduce several promising directions for future research in this area.
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Details
- Title
- The multisided complexity of fairness in recommender systems
- Creators
- Nasim Sonboli - University of Colorado BoulderRobin Burke - University of Colorado BoulderMichael Ekstrand - Boise State UniversityRishabh Mehrotra - Spotify, Inc., London, UK
- Publication Details
- The AI magazine, v 43(2), pp 164-176
- Publisher
- Wiley
- Number of pages
- 13
- Grant note
- National Science Foundation (IIS‐1911025)
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Information Science
- Web of Science ID
- WOS:000814699200004
- Scopus ID
- 2-s2.0-85134174762
- Other Identifier
- 991021818497904721
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Collaboration types
- Industry collaboration
- Domestic collaboration
- International collaboration
- Web of Science research areas
- Computer Science, Artificial Intelligence