揭示推荐系统中群体公平与个体公平的矛盾关系。
Stairway to Fairness: Connecting Group and Individual Fairness
- 统一评估指标，系统比较群体与个体公平性。
- 实验显示高群体公平推荐对个体可能极不公平。
- 为追求公平的推荐系统开发者提供关键参考。
推荐系统中的公平性通常分为群体公平与个体公平。然而，由于以往研究对两类公平性采用不同的评估指标或目标，缺乏科学的关联理解，导致两者间的关系尚不明确。本文通过统一的评估指标，全面比较两类公平性的衡量方式。在3个数据集上进行8次实验发现，高度满足群体公平的推荐结果，对个体而言可能极为不公。这一发现具有新颖性和实用性，为希望提升系统公平性的实践者提供了重要指导。代码已开源：https://github.com/theresiavr/stairway-to-fairness。
原文摘要 · Abstract (English)
Fairness in recommender systems (RSs) is commonly categorised into group fairness and individual fairness. However, there is no established scientific understanding of the relationship between the two fairness types, as prior work on both types has used different evaluation measures or evaluation objectives for each fairness type, thereby not allowing for a proper comparison of the two. As a result, it is currently not known how increasing one type of fairness may affect the other. To fill this gap, we study the relationship of group and individual fairness through a comprehensive comparison of evaluation measures that can be used for both fairness types. Our experiments with 8 runs across 3 datasets show that recommendations that are highly fair for groups can be very unfair for individuals. Our finding is novel and useful for RS practitioners aiming to improve the fairness of their systems. Our code is available at: https://github.com/theresiavr/stairway-to-fairness.
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