Stairway to Fairness: Connecting Group and Individual Fairness

Theresia Veronika Rampisela, Maria Maistro, Tuukka Ruotsalo, Falk Scholer, Christina Lioma
8/29/2025
cs.IRcs.AIcs.CL

Official code: theresiavr/stairway-to-fairness (2 stars, MIT, last commit 2025-10-10).

Abstract

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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Code Implementations(1)

theresiavr/stairway-to-fairnessOfficial100%
MIT

This hosts the code and appendix of the RecSys 2025 short paper "Stairway to Fairness: Connecting Group and Individual Fairness"

22Shell, PythonJun 30, 20251 years agoMIT
evaluationfairnessfairness-aifairness-assessmentfairness-metric+8 more

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Cite this paper

@article{rampisela2025stairway,
  title  = {Stairway to Fairness: Connecting Group and Individual Fairness},
  author = {Theresia Veronika Rampisela and Maria Maistro and Tuukka Ruotsalo and Falk Scholer and Christina Lioma},
  year   = {2025},
  eprint = {2508.21334},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2508.21334v1}
}

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