Dynamic fairness-aware recommendation through multi-agent social choice

TL;DR

This paper introduces a multi-agent social choice framework for dynamic, fairness-aware personalized recommendation systems that balances multiple stakeholder fairness concerns.

Contribution

It formalizes multistakeholder fairness as a two-stage social choice problem combining allocation and aggregation, advancing recommendation fairness methods.

Findings

Framework effectively integrates multiple fairness concerns

Simulations show dynamic fairness adaptation

New recommendation techniques derived from social choice model

Abstract

Algorithmic fairness in the context of personalized recommendation presents significantly different challenges to those commonly encountered in classification tasks. Researchers studying classification have generally considered fairness to be a matter of achieving equality of outcomes between a protected and unprotected group, and built algorithmic interventions on this basis. We argue that fairness in real-world application settings in general, and especially in the context of personalized recommendation, is much more complex and multi-faceted, requiring a more general approach. We propose a model to formalize multistakeholder fairness in recommender systems as a two stage social choice problem. In particular, we express recommendation fairness as a novel combination of an allocation and an aggregation problem, which integrate both fairness concerns and personalized recommendation provisions, and derive new recommendation techniques based on this formulation. Simulations demonstrate the ability of the framework to integrate multiple fairness concerns in a dynamic way.