Stairway to Fairness: Connecting Group and Individual Fairness
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Abstract (arXiv)
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.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 37 of its 71 references that are in the map. First posted on arXiv 29 Aug 2025.
Builds on
37 of 71 references.
References · 37
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25 more
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Citing papers per year: 2025: 1; 2026: 2.
3 papers.