Oct. 2023 talk at University of Glasgow
Title: Search, Recommendation, and Sea Monsters Ensuring that information access systems are “fair”, or that their benefits are equitably experienced by everyone they affect, is a complex, multi-faceted problem. Significant progress has been made in recent years on identifying and measuring important forms of unfair recommendation and retrieval, but there are still many ways that information systems can replicate, exacerbate, or mitigate potentially discriminatory harms that need careful study. These harms can affect different stakeholders — such as the producers and consumers of information, among others — in many different ways, including denying them access to the system’s benefits, misrepresenting them, or reinforcing unhelpful stereotypes. In this talk, I will provide an overview of the landscape of fairness and anti-discrimination in information access systems and their underlying theories, discussing both the state of the art in measuring relatively well-understood harms and new directions and open problems in defining and measuring fairness problems. Also available on YouTube. This talk draws heavily from our paper: 2022. Fairness in Information Access Systems. Foundations and Trends® in Information Retrieval 16(1–2) (July 11th, 2022), 1–177. DOI 10.1561/1500000079. arXiv:2105.05779 [cs.IR]. NSF PAR 10347630. Impact factor: 8. Cited 177 times. Cited 83 times. I also also discusses the following work we have published: 2023. Towards Measuring Fairness in Grid Layout in Recommender Systems. Presented at the 6th FAccTrec Workshop on Responsible Recommendation at RecSys 2023 (peer-reviewed but not archived). arXiv:2309.10271 [cs.IR]. Cited 1 time. 2023. Much Ado About Gender: Current Practices and Future Recommendations for Appropriate Gender-Aware Information Access. In Proceedings of the 2023 Conference on Human Information Interaction and Retrieval (CHIIR ’23). DOI 10.1145/3576840.3578316. arXiv:2301.04780. NSF PAR 10423693. Acceptance rate: 39.4%. Cited 18 times. Cited 11 times. 2022. Matching Consumer Fairness Objectives & Strategies for RecSys. Presented at the 5th FAccTrec Workshop on Responsible Recommendation at RecSys 2022 (peer-reviewed but not archived). arXiv:2209.02662 [cs.IR]. Cited 5 times. Cited 4 times. 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 [cs.IR]. Cited 10 times. Cited 5 times. 2022. Measuring Fairness in Ranked Results: An Analytical and Empirical Comparison. In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR ’22). pp. 726–736. DOI 10.1145/3477495.3532018. NSF PAR 10329880. Acceptance rate: 20%. Cited 57 times. Cited 44 times. 2022. The Multisided Complexity of Fairness in Recommender Systems. AI Magazine 43(2) (June 23rd, 2022), 164–176. DOI 10.1002/aaai.12054. NSF PAR 10334796. Cited 33 times. Cited 19 times. 2021. Exploring Author Gender in Book Rating and Recommendation. User Modeling and User-Adapted Interaction 31(3) (February 4th, 2021), 377–420. DOI 10.1007/s11257-020-09284-2. arXiv:1808.07586v2. NSF PAR 10218853. Impact factor: 4.412. Cited 195 times (shared with RecSys18◊). Cited 103 times (shared with RecSys18◊). 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 [cs.IR]. NSF PAR 10199451. Acceptance rate: 20%. Nominated for Best Long Paper. Cited 185 times. Cited 165 times. 2020. Enhancing Classroom Instruction with Online News. Aslib Journal of Information Management 72(5) (November 17th, 2020; online June 14th, 2020), 725–744. DOI 10.1108/AJIM-11-2019-0309. Impact factor: 1.903. Cited 19 times. Cited 11 times.Abstract
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