Decision-centric fairness: Evaluation and optimization for resource allocation problems

• URL: http://arxiv.org/abs/2504.20642v1
• Date: Tue, 29 Apr 2025 11:12:36 GMT
• Title: Decision-centric fairness: Evaluation and optimization for resource allocation problems
• Authors: Simon De Vos, Jente Van Belle, Andres Algaba, Wouter Verbeke, Sam Verboven,
• Abstract summary: We propose a decision-centric fairness methodology that induces fairness only within the decision-making region.<n>We empirically compare our approach to a global fairness approach on multiple (semi-synthetic) datasets.
• Score: 1.5623752145311105
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Data-driven decision support tools play an increasingly central role in decision-making across various domains. In this work, we focus on binary classification models for predicting positive-outcome scores and deciding on resource allocation, e.g., credit scores for granting loans or churn propensity scores for targeting customers with a retention campaign. Such models may exhibit discriminatory behavior toward specific demographic groups through their predicted scores, potentially leading to unfair resource allocation. We focus on demographic parity as a fairness metric to compare the proportions of instances that are selected based on their positive outcome scores across groups. In this work, we propose a decision-centric fairness methodology that induces fairness only within the decision-making region -- the range of relevant decision thresholds on the score that may be used to decide on resource allocation -- as an alternative to a global fairness approach that seeks to enforce parity across the entire score distribution. By restricting the induction of fairness to the decision-making region, the proposed decision-centric approach avoids imposing overly restrictive constraints on the model, which may unnecessarily degrade the quality of the predicted scores. We empirically compare our approach to a global fairness approach on multiple (semi-synthetic) datasets to identify scenarios in which focusing on fairness where it truly matters, i.e., decision-centric fairness, proves beneficial.

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