Abstract
Individual fairness is the principle aiming for equitable treatment for each individual affected by decisions. Despite its intuitive appeal, the practical applications of individual fairness for algorithmic decision-making systems remain relatively unexplored. In this paper, we investigate the consistency score metric and demonstrate how it fails to adequately capture fairness at the individual level, underscoring the need for a more fine-grained approach. We show that (1) the consistency score obscures instances where individuals are treated significantly differently to the individuals most similar to them and (2) the perceived fairness of individual decisions can be affected by several factors, including the similarity notion itself. To address these issues, we propose four new metrics that measure different aspects of the treatment of individuals with respect to
similar individuals, under varying similarity definitions. Our comprehensive evaluation of the new metrics shows that they offer a more nuanced approach to assessing individual fairness, enabling decision-makers to focus on individuals most adversely affected by controversial decisions.
similar individuals, under varying similarity definitions. Our comprehensive evaluation of the new metrics shows that they offer a more nuanced approach to assessing individual fairness, enabling decision-makers to focus on individuals most adversely affected by controversial decisions.
Original language | English |
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Title of host publication | ACM Conference on Fairness, Accountability, and Transparency 2025 |
Publisher | ACM |
Number of pages | 11 |
DOIs | |
Publication status | Published - Jun 2025 |