Abstract

The pursuit of algorithmic fairness requires that we think differently about the idea of the “user” in personalized systems, such as recommender systems. The conventional definition of the user in such systems focuses on the receiver of recommendations, the individual to whom a particular personalization output is directed. Fairness, especially provider-side fairness, requires that we consider a broader array of system users and stakeholders, whose needs, interests and preferences may need to be modeled. In this paper, we describe a framework in which the interests of providers and other stakeholders are represented as agents. These agents participate in the production of recommendations through a two-stage social choice mechanism. This approach has the benefit of being able to represent a wide variety of fairness concepts and to extend to multiple fairness concerns.

Reference

Burke, Robin, Nicholas Mattei, Vladislav Grozin, Amy Voida, and Nasim Sonboli. "Multi-agent social choice for dynamic fairness-aware recommendation." In Adjunct Proceedings of the 30th ACM Conference on User Modeling, Adaptation and Personalization, pp. 234-244. 2022.

Bibtex

@inproceedings{burke2022multi,
  title={Multi-agent social choice for dynamic fairness-aware recommendation},
  author={Burke, Robin and Mattei, Nicholas and Grozin, Vladislav and Voida, Amy and Sonboli, Nasim},
  booktitle={Adjunct Proceedings of the 30th ACM Conference on User Modeling, Adaptation and Personalization},
  pages={234--244},
  year={2022}
}