Doubly-Regressing Approach for Subgroup Fairness

• URL: http://arxiv.org/abs/2510.21091v1
• Date: Fri, 24 Oct 2025 02:04:44 GMT
• Title: Doubly-Regressing Approach for Subgroup Fairness
• Authors: Kyungseon Lee, Kunwoong Kim, Jihu Lee, Dongyoon Yang, Yongdai Kim,
• Abstract summary: As the number of sensitive attributes grows, the number of subgroups increases.<n>This creates heavy computational burdens and data sparsity problem.<n>We develop a novel learning algorithm for subgroup fairness.
• Score: 14.327714719028924
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Algorithmic fairness is a socially crucial topic in real-world applications of AI. Among many notions of fairness, subgroup fairness is widely studied when multiple sensitive attributes (e.g., gender, race, age) are present. However, as the number of sensitive attributes grows, the number of subgroups increases accordingly, creating heavy computational burdens and data sparsity problem (subgroups with too small sizes). In this paper, we develop a novel learning algorithm for subgroup fairness which resolves these issues by focusing on subgroups with sufficient sample sizes as well as marginal fairness (fairness for each sensitive attribute). To this end, we formalize a notion of subgroup-subset fairness and introduce a corresponding distributional fairness measure called the supremum Integral Probability Metric (supIPM). Building on this formulation, we propose the Doubly Regressing Adversarial learning for subgroup Fairness (DRAF) algorithm, which reduces a surrogate fairness gap for supIPM with much less computation than directly reducing supIPM. Theoretically, we prove that the proposed surrogate fairness gap is an upper bound of supIPM. Empirically, we show that the DRAF algorithm outperforms baseline methods in benchmark datasets, specifically when the number of sensitive attributes is large so that many subgroups are very small.

Related papers

• Fair Model-based Clustering [11.871560374559566]
  We propose a new fair clustering algorithm based on a finite mixture model, called Fair Model-based Clustering (FMC)<n>A main advantage of FMC is that the number of learnable parameters is independent of the sample size and thus can be scaled up easily.<n>FMC can be applied to non-metric data as long as the likelihood is well-defined.
  arXiv  Detail & Related papers  (2026-02-25T02:41:16Z)
• Finite-Sample and Distribution-Free Fair Classification: Optimal Trade-off Between Excess Risk and Fairness, and the Cost of Group-Blindness [14.421493372559762]
  We quantify the impact of enforcing algorithmic fairness and group-blindness in binary classification under group fairness constraints. We propose a unified framework for fair classification that provides distribution-free and finite-sample fairness guarantees with controlled excess risk.
  arXiv  Detail & Related papers  (2024-10-21T20:04:17Z)
• How does promoting the minority fraction affect generalization? A theoretical study of the one-hidden-layer neural network on group imbalance [64.1656365676171]
  Group imbalance has been a known problem in empirical risk minimization. This paper quantifies the impact of individual groups on the sample complexity, the convergence rate, and the average and group-level testing performance.
  arXiv  Detail & Related papers  (2024-03-12T04:38:05Z)
• A structured regression approach for evaluating model performance across intersectional subgroups [53.91682617836498]
  Disaggregated evaluation is a central task in AI fairness assessment, where the goal is to measure an AI system's performance across different subgroups. We introduce a structured regression approach to disaggregated evaluation that we demonstrate can yield reliable system performance estimates even for very small subgroups.
  arXiv  Detail & Related papers  (2024-01-26T14:21:45Z)
• Fairness Aware Counterfactuals for Subgroups [8.593488857185678]
  We present Fairness Aware Counterfactuals for Subgroups (FACTS), a framework for auditing subgroup fairness through counterfactual explanations. We aim to formulate different aspects of the difficulty of individuals in certain subgroups to achieve recourse. We introduce notions of subgroup fairness that are robust, if not totally oblivious, to the cost of achieving recourse.
  arXiv  Detail & Related papers  (2023-06-26T18:03:56Z)
• Feature Importance Disparities for Data Bias Investigations [2.184775414778289]
  It is widely held that one cause of downstream bias in classifiers is bias present in the training data. We present one such method that given a dataset $X$ consisting of protected and unprotected features, outcomes $y$, and a regressor $h$ that predicts $y$ given $X$. We show across $4$ datasets and $4$ common feature importance methods of broad interest to the machine learning community that we can efficiently find subgroups with large FID values even over exponentially large subgroup classes.
  arXiv  Detail & Related papers  (2023-03-03T04:12:04Z)
• Detection of Groups with Biased Representation in Ranking [28.095668425175564]
  We study the problem of detecting groups with biased representation in the top-$k$ ranked items. We propose efficient search algorithms for two different fairness measures.
  arXiv  Detail & Related papers  (2022-12-30T10:50:02Z)
• Synergies between Disentanglement and Sparsity: Generalization and Identifiability in Multi-Task Learning [79.83792914684985]
  We prove a new identifiability result that provides conditions under which maximally sparse base-predictors yield disentangled representations. Motivated by this theoretical result, we propose a practical approach to learn disentangled representations based on a sparsity-promoting bi-level optimization problem.
  arXiv  Detail & Related papers  (2022-11-26T21:02:09Z)
• Fairness via Adversarial Attribute Neighbourhood Robust Learning [49.93775302674591]
  We propose a principled underlineRobust underlineAdversarial underlineAttribute underlineNeighbourhood (RAAN) loss to debias the classification head.
  arXiv  Detail & Related papers  (2022-10-12T23:39:28Z)
• MultiFair: Multi-Group Fairness in Machine Learning [52.24956510371455]
  We study multi-group fairness in machine learning (MultiFair) We propose a generic end-to-end algorithmic framework to solve it. Our proposed framework is generalizable to many different settings.
  arXiv  Detail & Related papers  (2021-05-24T02:30:22Z)
• Fairness in Streaming Submodular Maximization: Algorithms and Hardness [20.003009252240222]
  We develop the first streaming approximation for submodular under fairness constraints, for both monotone and non-monotone functions. We validate our findings on DPP-based movie recommendation, clustering-based summarization, and maximum coverage in social networks.
  arXiv  Detail & Related papers  (2020-10-14T22:57:07Z)
• Data Augmentation Imbalance For Imbalanced Attribute Classification [60.71438625139922]
  We propose a new re-sampling algorithm called: data augmentation imbalance (DAI) to explicitly enhance the ability to discriminate the fewer attributes. Our DAI algorithm achieves state-of-the-art results, based on pedestrian attribute datasets.
  arXiv  Detail & Related papers  (2020-04-19T20:43:29Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.