FAIR: Fairness-Aware Information Retrieval Evaluation

TL;DR

This paper introduces FAIR, a new unified metric for evaluating fairness-aware information retrieval systems that balances user utility with fair exposure, and demonstrates its effectiveness through experiments.

Contribution

The paper proposes the FAIR metric that unifies relevance and fairness in IR evaluation and develops a ranking algorithm optimizing both criteria.

Findings

FAIR effectively highlights results with high utility and fairness.

The new metric relates well to existing utility and fairness measures.

The FAIR-based algorithm improves fairness without sacrificing utility.

Abstract

With the emerging needs of creating fairness-aware solutions for search and recommendation systems, a daunting challenge exists of evaluating such solutions. While many of the traditional information retrieval (IR) metrics can capture the relevance, diversity, and novelty for the utility with respect to users, they are not suitable for inferring whether the presented results are fair from the perspective of responsible information exposure. On the other hand, existing fairness metrics do not account for user utility or do not measure it adequately. To address this problem, we propose a new metric called FAIR. By unifying standard IR metrics and fairness measures into an integrated metric, this metric offers a new perspective for evaluating fairness-aware ranking results. Based on this metric, we developed an effective ranking algorithm that jointly optimized user utility and fairness. The experimental results showed that our FAIR metric could highlight results with good user utility and fair information exposure. We showed how FAIR related to a set of existing utility and fairness metrics and demonstrated the effectiveness of our FAIR-based algorithm. We believe our work opens up a new direction of pursuing a metric for evaluating and implementing the FAIR systems.