通往公平的阶梯：群体公平与个体公平的联结

摘要

推荐系统中的公平性通常被划分为群体公平性和个体公平性。然而，目前尚未建立对这两种公平性之间关系的科学理解，因为先前关于这两种公平性的研究采用了不同的评估指标或评估目标，从而无法对两者进行恰当的比较。因此，目前尚不清楚提升一种公平性可能会如何影响另一种公平性。为填补这一空白，我们通过全面比较适用于两种公平性的评估指标，研究了群体公平性与个体公平性之间的关系。我们在三个数据集上进行的八次实验表明，对群体高度公平的推荐可能对个体极为不公平。这一发现新颖且实用，对于旨在提升其系统公平性的推荐系统实践者具有重要意义。我们的代码可在以下网址获取：https://github.com/theresiavr/stairway-to-fairness。

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.

通往公平的阶梯：群体公平与个体公平的联结

Stairway to Fairness: Connecting Group and Individual Fairness

摘要

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