Matching Consumer Fairness Objectives & Strategies for RecSys

TL;DR

This paper emphasizes the importance of context-specific strategies for achieving consumer fairness in recommender systems, advocating for tailored interventions based on application details and fairness objectives.

Contribution

It highlights the need for nuanced, situation-aware fairness interventions in recommender systems, moving beyond one-size-fits-all approaches.

Findings

Different fairness strategies suit different application contexts

Tailored interventions improve fairness outcomes

Consumer fairness should be a creative, problem-specific process

Abstract

The last several years have brought a growing body of work on ensuring that recommender systems are in some sense consumer-fair -- that is, they provide comparable quality of service, accuracy of representation, and other effects to their users. However, there are many different strategies to make systems more fair and a range of intervention points. In this position paper, we build on ongoing work to highlight the need for researchers and practitioners to attend to the details of their application, users, and the fairness objective they aim to achieve, and adopt interventions that are appropriate to the situation. We argue that consumer fairness should be a creative endeavor flowing from the particularities of the specific problem to be solved.