Stairway to Fairness: Connecting Group and Individual Fairness
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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Theresia Veronika Rampisela
claimed_verified
2025-09-02T07:54:42.369Z
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/avatars/dbbf67f27593a510177dff06b00bf098.svg
TVR
user
theresiavr
68b5b820dc94ad77a7bfd034
Maria Maistro
68b5b820dc94ad77a7bfd035
Tuukka Ruotsalo
68b5b820dc94ad77a7bfd036
Falk Scholer
68b5b820dc94ad77a7bfd037
Christina Lioma
Stairway to Fairness: Connecting Group and Individual Fairness
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.
Experiments reveal that highly group-fair recommendations can be individually unfair, highlighting the need for a better understanding and comparison of fairness measures in recommender systems.
group fairness individual fairness evaluation measures recommender systems