Exploring Social Choice Mechanisms for Recommendation Fairness in SCRUF

TL;DR

This paper investigates social choice mechanisms within multi-agent recommender systems to address fairness concerns, demonstrating how different mechanisms influence fairness and accuracy tradeoffs and adapt to user dynamics.

Contribution

It introduces a social choice framework for fairness in recommender systems, exploring various choice mechanisms and their effects on fairness and accuracy.

Findings

Different choice mechanisms produce distinct fairness/accuracy tradeoffs.

Multi-agent formulation adapts effectively to user population changes.

Social choice algorithms offer flexible, multi-aspect fairness solutions.

Abstract

Fairness problems in recommender systems often have a complexity in practice that is not adequately captured in simplified research formulations. A social choice formulation of the fairness problem, operating within a multi-agent architecture of fairness concerns, offers a flexible and multi-aspect alternative to fairness-aware recommendation approaches. Leveraging social choice allows for increased generality and the possibility of tapping into well-studied social choice algorithms for resolving the tension between multiple, competing fairness concerns. This paper explores a range of options for choice mechanisms in multi-aspect fairness applications using both real and synthetic data and shows that different classes of choice and allocation mechanisms yield different but consistent fairness / accuracy tradeoffs. We also show that a multi-agent formulation offers flexibility in adapting to user population dynamics.