GESD: Beyond Outcome-Oriented Fairness

• URL: http://arxiv.org/abs/2605.15295v1
• Date: Thu, 14 May 2026 18:10:57 GMT
• Title: GESD: Beyond Outcome-Oriented Fairness
• Abstract summary: Group-level Explanation Stability Disparity (GESD) is a fairness metric that measures disparities in the stability, robustness, and sensitivity of model explanations.<n>Our code and datasets are available on GitHub.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Machine learning (ML) algorithms are increasingly deployed in high-stakes decision-making domains such as loan approvals, hiring, and recidivism predictions. While existing fairness metrics (e.g., statistical parity, equal opportunity) effectively quantify outcome-oriented disparities, they offer limited insight into the procedure or explanation behind biased decisions. To address this gap, we propose Group-level Explanation Stability Disparity (GESD), a \textit{procedural-oriented} fairness metric that measures disparities in the stability, robustness, and sensitivity of model explanations across different subgroups in a protected category. %GESD is explainer-agnostic, model-agnostic, and extends the scope of fairness analyses to the level of explainability. We further integrate GESD into a multi-objective optimization framework that jointly optimizes for utility, outcome-based fairness, and explanation-based fairness called FEU (Fairness--Explainability--Utility). Empirical results on multiple benchmark datasets show that GESD effectively captures group-wise discrepancies in explanation quality, and that FEU improves both utility and fairness over state-of-the-art methods. By bridging outcome-based and explanation-based fairness, GESD offers a comprehensive tool for diagnosing and mitigating bias in predictive modeling. Our code and datasets are available on GitHub {\hyperlink{https://github.com/horlahsunbo/GESD}{https://github.com/horlahsunbo/GESD}}

Related papers

• Statistical Properties of Robust Learning under Distributional Shifts [12.995907626827794]
  This paper studies generalization error in the target environment---the excess loss under the shifted target distribution.<n>We derive finite-sample generalization error bounds in the shifted target environment for both DRO and RS.<n>We apply the framework to a network lot-sizing problem, using it to interpret how robust policies respond to positive shifts in the demand distribution.
  arXiv  Detail & Related papers  (2026-08-13T12:06:48Z)
• Disparate Impact in Synthetic Data Generation [10.23887083601828]
  We revisit the notion of disparate impact for synthetic data generation.<n>We argue that non-disparate impact is achieved when the synthetic and real distributions are the same.
  arXiv  Detail & Related papers  (2026-06-11T09:33:12Z)
• From Insight to Action: A Novel Framework for Interpretability-Guided Data Selection in Large Language Models [73.72877445629383]
  Interpretability-Guided Data Selection (IGDS) is a framework that first identifies causal task features through frequency recall and interventional filtering.<n>We validate IGDS on mathematical reasoning, summarization, and translation tasks within Gemma-2, LLaMA-3.1, and Qwen3 models.
  arXiv  Detail & Related papers  (2026-04-28T03:16:24Z)
• MESD: Detecting and Mitigating Procedural Bias in Intersectional Groups [0.0]
  This research addresses an important gap by examining explainability with respect to fairness across multiple protected categories.<n> Experimental results across multiple datasets show that UEF effectively balances objectives.
  arXiv  Detail & Related papers  (2026-03-13T15:42:31Z)
• Reliable and Reproducible Demographic Inference for Fairness in Face Analysis [63.46525489354455]
  We propose a fully reproducible DAI pipeline that replaces conventional end-to-end training with a modular transfer learning approach.<n>We audit this pipeline across three dimensions: accuracy, fairness, and a newly introduced notion of robustness, defined via intra-identity consistency.<n>Our results show that the proposed method outperforms strong baselines, particularly on ethnicity, which is the more challenging attribute.
  arXiv  Detail & Related papers  (2025-10-23T12:22:02Z)
• NDCG-Consistent Softmax Approximation with Accelerated Convergence [67.10365329542365]
  We propose novel loss formulations that align directly with ranking metrics.<n>We integrate the proposed RG losses with the highly efficient Alternating Least Squares (ALS) optimization method.<n> Empirical evaluations on real-world datasets demonstrate that our approach achieves comparable or superior ranking performance.
  arXiv  Detail & Related papers  (2025-06-11T06:59:17Z)
• Knowledge Distillation and Enhanced Subdomain Adaptation Using Graph Convolutional Network for Resource-Constrained Bearing Fault Diagnosis [0.0]
  We propose a progressive knowledge distillation framework that transfers knowledge from a complex teacher model to a compact and efficient student model.<n>We introduce Enhanced Local Maximum Mean Squared Discrepancy (ELMMSD), which leverages mean and variance statistics in the Reproducing Kernel Hilbert Space (RKHS) and incorporates a priori probability distributions between labels.
  arXiv  Detail & Related papers  (2025-01-13T10:05:47Z)
• LaPLACE: Probabilistic Local Model-Agnostic Causal Explanations [1.0370398945228227]
  We introduce LaPLACE-explainer, designed to provide probabilistic cause-and-effect explanations for machine learning models. The LaPLACE-Explainer component leverages the concept of a Markov blanket to establish statistical boundaries between relevant and non-relevant features. Our approach offers causal explanations and outperforms LIME and SHAP in terms of local accuracy and consistency of explained features.
  arXiv  Detail & Related papers  (2023-10-01T04:09:59Z)
• Dr. FERMI: A Stochastic Distributionally Robust Fair Empirical Risk Minimization Framework [12.734559823650887]
  In the presence of distribution shifts, fair machine learning models may behave unfairly on test data. Existing algorithms require full access to data and cannot be used when small batches are used. This paper proposes the first distributionally robust fairness framework with convergence guarantees that do not require knowledge of the causal graph.
  arXiv  Detail & Related papers  (2023-09-20T23:25:28Z)
• Chasing Fairness Under Distribution Shift: A Model Weight Perturbation Approach [72.19525160912943]
  We first theoretically demonstrate the inherent connection between distribution shift, data perturbation, and model weight perturbation. We then analyze the sufficient conditions to guarantee fairness for the target dataset. Motivated by these sufficient conditions, we propose robust fairness regularization (RFR)
  arXiv  Detail & Related papers  (2023-03-06T17:19:23Z)
• Causal Feature Selection for Algorithmic Fairness [61.767399505764736]
  We consider fairness in the integration component of data management. We propose an approach to identify a sub-collection of features that ensure the fairness of the dataset.
  arXiv  Detail & Related papers  (2020-06-10T20:20:10Z)
• Stochastic-Sign SGD for Federated Learning with Theoretical Guarantees [49.91477656517431]
  Quantization-based solvers have been widely adopted in Federated Learning (FL) No existing methods enjoy all the aforementioned properties. We propose an intuitively-simple yet theoretically-simple method based on SIGNSGD to bridge the gap.
  arXiv  Detail & Related papers  (2020-02-25T15:12:15Z)

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.