Fair Recommender Systems
In this project, we are investigating several questions of fairness and bias in recommender systems:
- What does it mean for a recommender to be fair, unfair, or biased?
- What potentially discriminatory biases are present in the recommender’s input data, algorithmic structure, or output?
- How do these biases change over time through the recommender-user feedback loop?
This is a part of our overall, ongoing goal to help make recommenders (and other AI systems) better for the people they affect. I have been involved with several workshops, sessions, etc. related to fair recommendation. 2023. Patterns of Gender-Specializing Query Reformulation. To appear as a short paper in ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR ’23). DOI 10.1145/3539618.3592034. arXiv:2304.13129. 2023. Much Ado About Gender: Current Practices and Future Recommendations for Appropriate Gender-Aware Information Access. In ACM SIGIR Conference on Human Information Interaction and Retrieval (CHIIR ’23). DOI 10.1145/3576840.3578316. arXiv:2301.04780. Acceptance rate: 39.4%. Cited 1 time. 2022. Matching Consumer Fairness Objectives & Strategies for RecSys. Presented at the 5th FAccTrec Workshop on Responsible Recommendation (peer-reviewed but not archived). DOI 10.48550/arXiv.2209.02662. arXiv:2209.02662. 2022. Fire Dragon and Unicorn Princess: Gender Stereotypes and Children’s Products in Search Engine Responses. In SIGIR eCom ’22. DOI 10.48550/arXiv.2206.13747. arXiv:2206.13747. Cited 2 times. 2022. Fairness in Information Access Systems. Foundations and Trends® in Information Retrieval 16(1–2) (July 2022), 1–177. DOI 10.1561/1500000079. arXiv:2105.05779. Impact factor: 8. Cited 28 times. Cited 24 times. 2022. The Multisided Complexity of Fairness in Recommender Systems. AI Magazine 43(2) (June 2022), 164–176. DOI 10.1002/aaai.12054. NSF PAR 10334796. Cited 5 times. Cited 3 times. 2022. Fairness in Recommender Systems. In Recommender Systems Handbook (3rd edition). Francesco Ricci, Lior Roach, and Bracha Shapira, eds. Springer-Verlag. DOI 10.1007/978-1-0716-2197-4_18. ISBN 978-1-0716-2196-7. Cited 4 times. Cited 7 times. 2021. Exploring Author Gender in Book Rating and Recommendation. User Modeling and User-Adapted Interaction 31(3) (February 2021), 377–420. DOI 10.1007/s11257-020-09284-2. NSF PAR 10218853. Impact factor: 4.412. Cited 114* times. 2021. Estimation of Fair Ranking Metrics with Incomplete Judgments. In Proceedings of The Web Conference 2021 (TheWebConf 2021). ACM. DOI 10.1145/3442381.3450080. arXiv:2108.05152. NSF PAR 10237411. Acceptance rate: 21%. Cited 22 times. Cited 21 times. 2020. Evaluating Stochastic Rankings with Expected Exposure. In Proceedings of the 29th ACM International Conference on Information and Knowledge Management (CIKM ’20). ACM, pp. 275–284. DOI 10.1145/3340531.3411962. arXiv:2004.13157. NSF PAR 10199451. Acceptance rate: 20%. Nominated for Best Long Paper. Cited 93 times. Cited 89 times. 2020. Comparing Fair Ranking Metrics. Presented at the 3rd FAccTrec Workshop on Responsible Recommendation (peer-reviewed but not archived). DOI 10.48550/arXiv.2009.01311. arXiv:2009.01311. Cited 15 times. Cited 16 times. 2020. Overview of the TREC 2019 Fair Ranking Track. In The Twenty-Eighth Text REtrieval Conference (TREC 2019) Proceedings (TREC 2019). DOI 10.48550/arXiv.2003.11650. arXiv:2003.11650. Cited 23 times. Cited 27 times. 2019. FACTS-IR: Fairness, Accountability, Confidentiality, Transparency, and Safety in Information Retrieval. SIGIR Forum 53(2) (December 2019), 20–43. DOI 10.1145/3458553.3458556. Cited 4 times. Cited 14 times. 2018. Exploring Author Gender in Book Rating and Recommendation. In Proceedings of the 12th ACM Conference on Recommender Systems (RecSys ’18). ACM, pp. 242–250. DOI 10.1145/3240323.3240373. arXiv:1808.07586v1. Acceptance rate: 17.5%. Cited 114 times. Citations reported under UMUAI21*. 2018. All The Cool Kids, How Do They Fit In?: Popularity and Demographic Biases in Recommender Evaluation and Effectiveness. In Proceedings of the 1st Conference on Fairness, Accountability and Transparency (FAT* 2018). PMLR, Proceedings of Machine Learning Research 81:172–186. Acceptance rate: 24%. Cited 146 times. Cited 154 times. 2017. The Demographics of Cool: Popularity and Recommender Performance for Different Groups of Users. In RecSys 2017 Poster Proceedings. CEUR, Workshop Proceedings 1905. Cited 5 times. Cited 14 times. 2020. 3rd FATREC Workshop: Responsible Recommendation. In Proceedings of the 14th ACM Conference on Recommender Systems (RecSys ’20). ACM. DOI 10.1145/3383313.3411538. Cited 4 times. Cited 4 times. 2020. FairUMAP 2020: The 3rd Workshop on Fairness in User Modeling, Adaptation and Personalization. In Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization (UMAP ’20). ACM. DOI 10.1145/3340631.3398671. Cited 2 times. Cited 2 times. 2019. Workshop on Fairness, Accountability, Confidentiality, Transparency, and Safety in Information Retrieval (FACTS-IR). In Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR ’19). ACM. DOI 10.1145/3331184.3331644. Cited 7 times. Cited 24 times. 2019. FairUMAP 2019 Chairs’ Welcome Overview. In Adjunct Publication of the 27th Conference on User Modeling, Adaptation and Personalization (UMAP ’19). ACM. DOI 10.1145/3314183.3323842. 2018. 2nd FATREC Workshop: Responsible Recommendation. In Proceedings of the 12th ACM Conference on Recommender Systems (RecSys ’18). ACM. DOI 10.1145/3240323.3240335. Cited 8 times. Cited 10 times. 2018. UMAP 2018 Fairness in User Modeling, Adaptation and Personalization (FairUMAP 2018) Chairs’ Welcome & Organization. In Adjunct Publication of the 26th Conference on User Modeling, Adaptation, and Personalization (UMAP ’18). ACM. DOI 10.1145/3213586.3226200. 2017. The FATREC Workshop on Responsible Recommendation. In Proceedings of the 11th ACM Conference on Recommender Systems (RecSys ’17). ACM. DOI 10.1145/3109859.3109960. Cited 11 times.Blog Posts and Other Coverage
Funding
Workshops and Meetings
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Workshop Summaries