Stairway to Fairness: Connecting Group and Individual Fairness
This addresses a gap in fairness understanding for recommender system practitioners, though it is incremental as it focuses on comparing existing measures rather than proposing new solutions.
The paper investigates the relationship between group and individual fairness in recommender systems, finding that recommendations highly fair for groups can be very unfair for individuals based on experiments with 8 runs across 3 datasets.
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.