{"type":"ScholarlyArticle","title":"Online Certification of Preference-Based Fairness for Personalized Recommender Systems (Extended Abstract)","authors":[{"name":"Virginie Do","slug":"virginie-do","given":"Virginie","family":"Do","affiliation":"LAMSADE , Université PSL , Université Paris Dauphine , CNRS , France"},{"name":"Sam Corbett-Davies","slug":"sam-corbett-davies","given":"Sam","family":"Corbett-Davies","affiliation":"LAMSADE , Université PSL , Université Paris Dauphine , CNRS , France"},{"name":"Jamal Atif","slug":"jamal-atif","given":"Jamal","family":"Atif","affiliation":"LAMSADE , Université PSL , Université Paris Dauphine , CNRS , France"},{"name":"Nicolas Usunier","slug":"nicolas-usunier","given":"Nicolas","family":"Usunier","affiliation":"LAMSADE , Université PSL , Université Paris Dauphine , CNRS , France"}],"abstract":"Recommender systems are facing scrutiny because of their growing impact on the opportunities we have access to. Current audits for fairness are limited to coarse-grained parity assessments at the level of sensitive groups. We propose to audit for envy-freeness, a more granular criterion aligned with individual preferences: every user should prefer their recommendations to those of other users. Since auditing for envy requires to estimate the preferences of users beyond their existing recommendations, we cast the audit as a new pure exploration problem in multi-armed bandits. We propose a sample-efficient algorithm with theoretical guarantees that it does not deteriorate user experience. We also study the trade-offs achieved on real-world recommendation datasets.","doi":"10.24963/ijcai.2023/714","url":"https://ijcai.org/proceedings/2023/714","pdf":"https://ijcai.org/proceedings/2023/0714.pdf","markdown":"https://ijcai.org/proceedings/2023/714.md","bibtex":"https://ijcai.org/proceedings/2023/bibtex/714","volume":{"slug":"2023","title":"Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2023","url":"https://ijcai.org/proceedings/2023/","year":2023,"location":"Macao, SAR","dates":"19-25 August 2023","editors":"Edith Elkind","isbn":"978-1-956792-03-4","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2023"},"section":"Sister Conferences Best Papers","track":"Sister Conferences Best Papers","number":714,"pages":{"first":6426,"last":6430},"keywords":["Sister Conferences Best Papers: AI Ethics, Trust, Fairness","Sister Conferences Best Papers: Machine Learning"],"videos":[],"references":[{"position":1,"raw":"[Audibert et al., 2010] Jean-Yves Audibert, Sébastien Bubeck, and Remi Munos. 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