Fairness-informed Pareto Optimization : An Efficient Bilevel Framework

• URL: http://arxiv.org/abs/2601.13448v2
• Date: Thu, 22 Jan 2026 13:03:55 GMT
• Title: Fairness-informed Pareto Optimization : An Efficient Bilevel Framework
• Authors: Sofiane Tanji, Samuel Vaiter, Yassine Laguel,
• Abstract summary: We present BADR, a framework to recover the optimal model for any fairness metric.<n>We equip BADR with two novel large-scale, single-loop algorithms, BADR-GD and BADR-SGD.<n>Badr is an open-source Python toolbox implementing our framework for a variety of learning tasks and fairness metrics.
• Score: 9.47506642944168
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Despite their promise, fair machine learning methods often yield Pareto-inefficient models, in which the performance of certain groups can be improved without degrading that of others. This issue arises frequently in traditional in-processing approaches such as fairness-through-regularization. In contrast, existing Pareto-efficient approaches are biased towards a certain perspective on fairness and fail to adapt to the broad range of fairness metrics studied in the literature. In this paper, we present BADR, a simple framework to recover the optimal Pareto-efficient model for any fairness metric. Our framework recovers its models through a Bilevel Adaptive Rescalarisation procedure. The lower level is a weighted empirical risk minimization task where the weights are a convex combination of the groups, while the upper level optimizes the chosen fairness objective. We equip our framework with two novel large-scale, single-loop algorithms, BADR-GD and BADR-SGD, and establish their convergence guarantees. We release badr, an open-source Python toolbox implementing our framework for a variety of learning tasks and fairness metrics. Finally, we conduct extensive numerical experiments demonstrating the advantages of BADR over existing Pareto-efficient approaches to fairness.

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