# Stairway to Fairness: Connecting Group and Individual Fairness

## TL;DR

This paper investigates the relationship between group and individual fairness in recommender systems, revealing that optimizing for one can lead to unfairness in the other, and provides a comprehensive evaluation framework.

## Contribution

It introduces a systematic comparison of fairness measures for group and individual fairness, highlighting their trade-offs and interactions.

## Key findings

- High group fairness can lead to individual unfairness.
- Evaluation measures for both fairness types are comparable.
- Insights aid practitioners in balancing fairness objectives.

## 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.