Stairway to Fairness: Connecting Group and Individual Fairness
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866912558845263872 |
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| author | Rampisela, Theresia Veronika Maistro, Maria Ruotsalo, Tuukka Scholer, Falk Lioma, Christina |
| author_facet | Rampisela, Theresia Veronika Maistro, Maria Ruotsalo, Tuukka Scholer, Falk Lioma, Christina |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_21334 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Stairway to Fairness: Connecting Group and Individual Fairness Rampisela, Theresia Veronika Maistro, Maria Ruotsalo, Tuukka Scholer, Falk Lioma, Christina Information Retrieval Artificial Intelligence Computation and Language Computers and Society 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. |
| title | Stairway to Fairness: Connecting Group and Individual Fairness |
| topic | Information Retrieval Artificial Intelligence Computation and Language Computers and Society |
| url | https://arxiv.org/abs/2508.21334 |