通往公平的階梯：群體與個體公平性的連結

摘要

推薦系統（RSs）中的公平性通常被分為群體公平性和個體公平性兩類。然而，目前尚未建立對這兩種公平性之間關係的科學理解，因為先前關於這兩種公平性的研究各自使用了不同的評估指標或評估目標，從而無法對兩者進行適當的比較。因此，目前尚不清楚提高一種公平性可能會如何影響另一種公平性。為填補這一空白，我們通過對可用於兩種公平性的評估指標進行全面比較，來研究群體公平性和個體公平性之間的關係。我們在三個數據集上進行的八次實驗表明，對群體高度公平的推薦可能對個體非常不公平。這一發現對於旨在提高其系統公平性的推薦系統實踐者來說是新穎且有用的。我們的代碼可在以下網址獲取：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.

通往公平的階梯：群體與個體公平性的連結

Stairway to Fairness: Connecting Group and Individual Fairness

摘要

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