Multisided Fairness for Recommendation

TL;DR

This paper explores multisided fairness in recommendation systems, proposing a taxonomy and architectures to ensure fair outcomes for multiple stakeholders, addressing a complex aspect of fairness in machine learning.

Contribution

It introduces the concept of multisided fairness in recommendation systems and provides a taxonomy and architectural suggestions for fairness-aware recommenders.

Findings

Proposes a taxonomy of fairness-aware recommender systems

Highlights the importance of multisided fairness in recommendations

Suggests architectures for implementing fairness in recommendation systems

Abstract

Recent work on machine learning has begun to consider issues of fairness. In this paper, we extend the concept of fairness to recommendation. In particular, we show that in some recommendation contexts, fairness may be a multisided concept, in which fair outcomes for multiple individuals need to be considered. Based on these considerations, we present a taxonomy of classes of fairness-aware recommender systems and suggest possible fairness-aware recommendation architectures.