The multisided complexity of fairness in recommender systems. Issue 2 (23rd June 2022)

• Record Type: Journal Article
• Authors: Sonboli, Nasim; Burke, Robin; Ekstrand, Michael; Mehrotra, Rishabh
• Is Part Of: AI magazine. Volume 43:Issue 2 (2022)
• Page Start: 164
• Page End: 176
• Publication Date: 2022-06-23
• Subjects: Artificial intelligence -- Periodicals; Artificial intelligence -- Computer programs -- Periodicals; System design -- Periodicals; Artificial intelligence -- Technological innovations -- Periodicals; Artificial intelligence -- Industrial applications -- Periodicals; Artificial intelligence -- Educational applications -- Periodicals; Intelligence artificielle -- Périodiques; Intelligence artificielle -- Logiciels -- Périodiques; Conception de systèmes -- Périodiques; Intelligence artificielle -- Innovations -- Périodiques; Intelligence artificielle -- Applications industrielles -- Périodiques; Intelligence artificielle -- Applications en éducation -- Périodiques; Artificial intelligence; Artificial intelligence -- Computer programs; Artificial intelligence -- Educational applications; Artificial intelligence -- Industrial applications; System design; Kunstmatige intelligentie; Electronic journals; Periodicals; Periodicals; 006.305
• DOI: 10.1002/aaai.12054
• Languages: English
• ISSNs: 0738-4602

Abstract

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.