Fairness and Transparency in Recommendation: The Users' Perspective

TL;DR

This paper explores user perceptions of fairness in recommender systems and proposes transparency features to improve understanding and trust in fairness-aware recommendations.

Contribution

It provides the first user-centered analysis of fairness in recommender systems and introduces design features to enhance transparency based on user interviews.

Findings

Users value transparency about fairness objectives.

Three proposed features can improve user understanding.

Enhanced transparency may increase trust in recommendations.

Abstract

Though recommender systems are defined by personalization, recent work has shown the importance of additional, beyond-accuracy objectives, such as fairness. Because users often expect their recommendations to be purely personalized, these new algorithmic objectives must be communicated transparently in a fairness-aware recommender system. While explanation has a long history in recommender systems research, there has been little work that attempts to explain systems that use a fairness objective. Even though the previous work in other branches of AI has explored the use of explanations as a tool to increase fairness, this work has not been focused on recommendation. Here, we consider user perspectives of fairness-aware recommender systems and techniques for enhancing their transparency. We describe the results of an exploratory interview study that investigates user perceptions of fairness, recommender systems, and fairness-aware objectives. We propose three features -- informed by the needs of our participants -- that could improve user understanding of and trust in fairness-aware recommender systems.