Trustworthy AI Fulbright Alumni '26 Meeting Tyrsak Elena Štefancová [email protected] Fulbright Visiting Student Researcher ‘23 

Recommendation Systems ● software tools and techniques that are used to provide item suggestions (recommended objects) that are most likely to be preferred by a particular user users as U, items as I ● outcome ○ form of a list of items ordered based on the predicted usefulness 

prof. Robin Burke and fairness-aware RecSys 

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Fairness Challenges ● reducing bias from input data towards certain groups of stakeholders ● multiple fairness concerns ○ each potentially formulated in a different way, relevant to a different set of stakeholders 

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Publications Amanda Aird, Elena Štefancová, Anas Buhayh, Cassidy All, Martin Homola, Nicholas Mattei, and Robin Burke. 2025. Integrating Individual and Group Fairness for Recommender Systems through Social Choice. In Proceedings of the Nineteenth ACM Conference on Recommender Systems (RecSys '25). Association for Computing Machinery, New York, NY, USA, 177–186. https://doi.org/10.1145/3705328.3748087 Amanda Aird, Paresha Farastu, Joshua Sun, Elena Stefancová, Cassidy All, Amy Voida, Nicholas Mattei, and Robin Burke. 2024. Dynamic Fairness-aware Recommendation Through Multi-agent Social Choice. ACM Trans. Recomm. Syst. 3, 2, Article 21 (June 2025), 35 pages. https://doi.org/10.1145/3690653 Elena Stefancova, Cassidy All, Joshua Paup, Martin Homola, Nicholas Mattei, and Robin Burke. 2024. Data generation via latent factor simulation for fairness-aware re-ranking. ArXiv, abs/2409.14078. Amanda Aird, Elena Štefancová, Cassidy All, Amy Voida, Martin Homola, Nicholas Mattei, and Robin Burke. 2024. Social Choice for Heterogeneous Fairness in Recommendation. In Proceedings of the 18th ACM Conference on Recommender Systems (RecSys '24). Association for Computing Machinery, New York, NY, USA, 1096–1101. https://doi.org/10.1145/3640457.3691706 AIRD, Amanda; ALL, Cassidy; FARASTU, Paresha; STEFANCOVA, Elena; SUN, Joshua; MATTEI, Nicholas; BURKE, Robin. 2023. Exploring Social Choice Mechanisms for Recommendation Fairness in SCRUF. https://arxiv.org/abs/2309.08621 

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Fulbright Visiting Student Researcher ‘23 University of Colorado Boulder CMDI Department of Information Science (INFO) Prof. Robin Burke ThatRecSys Lab 

SCRUF-D: Social Choice for Recommendation Under Fairness - Dynamic 1. a user arrives 2. agents express their preferences for being matched with this user 3. allocation mechanism receives the agents’ scores 4. the system generates personalised recommendations for the user 5. each of the allocated agents outputs their ranking of the items 6. a choice function aggregates the produced lists into a final list of recommended items