Fair recommendation (and related problems, such as fair information retrieval)
is a complex, multi-faceted problem. Significant progress has been made in
recent years on identifying and measuring important forms of unfair
recommendation, but there are still many ways recommender systems can replicate,
exacerbate, or mitigate potentially discriminatory harms that need careful
In this talk, I will provide an overview of the landscape of fairness and
anti-discrimination in information access systems, 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. This will set the
workshop's metrics and objectives in a broad context and hopefully catalyze
discussion about what the next iteration of EasyRS and research following up on
conference outcomes might look like.
Lequn Wang and Thorsten Joachims. 2021. “User Fairness, Item Fairness, and
Diversity for Rankings in Two-Sided Markets”. In Proceedings of the 2021 ACM
SIGIR International Conference on Theory of Information Retrieval (ICTIR ’21).
ACM, 23–41. doi:10.1145/3471158.3472260.
Lex Beattie, Dan Taber, and Henriette Cramer. 2022. “Challenges in Translating Research to Practice for Evaluating Fairness and Bias in Recommendation Systems.” In Proceedings of the 16th ACM Conference on Recommender Systems (RecSys ’22). doi:10.1145/3523227.3547403.
Beutel, Alex, Ed H. Chi, Cristos Goodrow, Jilin Chen, Tulsee Doshi, Hai Qian, Li Wei, et al. 2019. “Fairness in Recommendation Ranking through Pairwise Comparisons.” In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. ACM Press. doi:10.1145/3292500.3330745.
Beutel, Alex, Jilin Chen, Zhe Zhao, and Ed H. Chi. 2017. “Data Decisions and Theoretical Implications When Adversarially Learning Fair Representations.” arXiv [cs.LG]. http://arxiv.org/abs/1707.00075.
Benjamin Fish, Ashkan Bashardoust, Danah Boyd, Sorelle Friedler, Carlos Scheidegger, and Suresh Venkatasubramanian. 2019. “Gaps in Information Access in Social Networks?” In WWW ’19: The World Wide Web Conferencedoi:10.1145/3308558.3313680.
Mitchell, Shira, Eric Potash, Solon Barocas, Alexander D’Amour, and Kristian Lum. 2020. “Algorithmic Fairness: Choices, Assumptions, and Definitions.” Annual Review of Statistics and Its Application 8 (November). doi:10.1146/annurev-statistics-042720-125902.
Biega, A.J., Gummadi, K. P., and Weikum, G. 2018. Equity of Attention: Amortizing Individual Fairness in Rankings. In Proceedings of the 41st International ACM SIGIR Conference on Research & Development in Information Retrieval, ACM. doi:10.1145/3209978.3210063.
Ben Green and Salomé Viljoen. 2020. Algorithmic Realism: Expanding the
Boundaries of Algorithmic Thought. In Conference on Fairness, Accountability, and Transparency (FAT* ’20) doi:10.1145/3351095.3372840.
Andrew D. Selbst, Danah Boyd, Sorelle A. Friedler, Suresh Venkatasubramanian, and Janet Vertesi. 2019. “Fairness and Abstraction in Sociotechnical Systems.” In Proceedings of the Conference on Fairness, Accountability, and Transparency (FAT* ’19). doi:10.1145/3287560.3287598.
Sorelle A. Friedler, Carlos Scheidegger, and Suresh Venkatasubramanian. 2021. “The (Im)possibility of Fairness.” Communications of the ACM 64 (4): 136–43. doi:10.1145/3433949.
Alexandra Chouldechova. 2017. “Fair Prediction with Disparate Impact: A Study of Bias in Recidivism Prediction Instruments.” Big Data 5 (2): 153–63. doi:10.1089/big.2016.0047.
Reuben Binns. 2020. “On the Apparent Conflict between Individual and Group Fairness.” In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency. (FAT* ’20). doi:10.1145/3351095.3372864.