Social Choice for Heterogeneous Fairness in Recommendation
<|reference_start|>Social Choice for Heterogeneous Fairness in Recommendation: Algorithmic fairness in recommender systems requires close attention to the needs of a diverse set of stakeholders that may have competing interests. Previous work in this area has often been limited by fixed, single-objective definitions of fairness, built into algorithms or optimization criteria that are applied to a single fairness dimension or, at most, applied identically across dimensions. These narrow conceptualizations limit the ability to adapt fairness-aware solutions to the wide range of stakeholder needs and fairness definitions that arise in practice. Our work approaches recommendation fairness from the standpoint of computational social choice, using a multi-agent framework. In this paper, we explore the properties of different social choice mechanisms and demonstrate the successful integration of multiple, heterogeneous fairness definitions across multiple data sets.<|reference_end|>
@article{aird2024social,
    title={Social Choice for Heterogeneous Fairness in Recommendation},
    author={Amanda Aird, Elena v{S}tefancov'a, Cassidy All, Amy Voida, Martin
  Homola, Nicholas Mattei, Robin Burke},
    journal={arXiv preprint arXiv:2410.04551},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.04551},
    primaryClass={cs.IR cs.CY cs.LG}
}