Abstract

Every day, people use recommender systems, search engines, generative AI tools, and other information access systems to locate, collate, and understand information. These tools in turn shape their users’ understanding of the information space, and through it the world and society they inhabit. Information access has profound possibilities for connecting people with information, products, culture, and people they wouldn’t encounter in other ways, but can also reproduce a range of harms including discrimination, negative or unnecessary stereotypes, information silos, and more. I am concerned with ensuring these systems and their effects are beneficial: that their benefits are available to everyone, they are built on and with principles upholding human rights and dignity, and they are accountable to the people they will impact.

In this talk, I will discuss efforts to map the landscape of social impacts and harms and to achieve the positive promise of effective and equitable information access, with a particular emphasis on developing constructs and measurements to assess the extent to which information access systems advance or impede efforts to build inclusive, equitable, and democratic societies.

Papers

Michael D. Ekstrand, Lex Beattie, Maria Soledad Pera, and Henriette Cramer. 2024. Not Just Algorithms: Strategically Addressing Consumer Impacts in Information Retrieval. In Proceedings of the 46th European Conference on Information Retrieval (ECIR ’24, IR for Good track), Mar 24–28, 2024. Lecture Notes in Computer Science 14611:314–335. DOI 10.1007/978-3-031-56066-8_25. NSF PAR 10497110. Acceptance rate: 35.9%. Cited 14 times. Cited 6 times.

Michael D. Ekstrand, Anubrata Das, Robin Burke, and Fernando Diaz. 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 [cs.IR]. NSF PAR 10347630. Impact factor: 8. Cited 229 times. Cited 108 times.

Michael D. Ekstrand, Ben Carterette, and Fernando Diaz. 2024. Distributionally-Informed Recommender System Evaluation. Transactions on Recommender Systems 2(1) (March 2024; online Aug 4, 2023), 6:1–27. DOI 10.1145/3613455. arXiv:2309.05892 [cs.IR]. NSF PAR 10461937. Cited 19 times. Cited 11 times.

Amifa Raj and Michael D. Ekstrand. 2022. Fire Dragon and Unicorn Princess: Gender Stereotypes and Children’s Products in Search Engine Responses. In SIGIR eCom ’22, Jul 15, 2022. 9 pp.  DOI 10.48550/arXiv.2206.13747. arXiv:2206.13747 [cs.IR]. Cited 13 times. Cited 6 times.

Michael D. Ekstrand and Daniel Kluver. 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. arXiv:1808.07586v2. NSF PAR 10218853. Impact factor: 4.412. Cited 217 times (shared with RecSys18. Cited 117 times (shared with RecSys18.

Christine Pinney, Amifa Raj, Alex Hanna, and Michael D. Ekstrand. 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), Mar 19, 2023. pp. 269–279. DOI 10.1145/3576840.3578316. arXiv:2301.04780. NSF PAR 10423693. Acceptance rate: 39.4%. Cited 27 times. Cited 15 times.

Other Citations

• Burke, Robin, and Morgan Sylvester. 2024. “Post-Userist Recommender Systems: A Manifesto.” <arXiv:2410.11870>.
• Green, Ben, and Salomé Viljoen. 2020. “Algorithmic Realism: Expanding the Boundaries of Algorithmic Thought.” FAT* ’20. doi:10.1145/3351095.3372840.
• Hill, William, Larry Stead, Mark Rosenstein, and George Furnas. 1995. “Recommending and Evaluating Choices in a Virtual Community of Use.” In CHI ’95: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, 194–201. doi:10.1145/223904.223929.
• Sapiezynski, Piotr, Wesley Zeng, Ronald E Robertson, Alan Mislove, and Christo Wilson. 2019. “Quantifying the Impact of User Attention on Fair Group Representation in Ranked Lists.” Companion Proceedings of The 2019 World Wide Web Conference, WWW ’19 Companion, May 13, 553–62. https://doi.org/10.1145/3308560.3317595.
• Smith, Jessie J., Lex Beattie, and Henriette Cramer. 2023. “Scoping Fairness Objectives and Identifying Fairness Metrics for Recommender Systems: The Practitioners’ Perspective.” In Proceedings of the ACM Web Conference 2023, 3648–59. doi:10.1145/3543507.3583204.