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Tag: fairness

  • Fairness under Graph Uncertainty: Achieving Interventional Fairness with Partially Known Causal Graphs over Clusters of Variables

    Fairness under Graph Uncertainty: Achieving Interventional Fairness with Partially Known Causal Graphs over Clusters of Variables arXiv:2602.23611v1 Announce Type: new Abstract: Algorithmic decisions about individuals require predictions that are not only accurate but also fair with respect to sensitive attributes such as gender and race. Causal notions of fairness align with legal requirements, yet many…

  • Beyond Procedure: Substantive Fairness in Conformal Prediction

    Beyond Procedure: Substantive Fairness in Conformal Prediction arXiv:2602.16794v1 Announce Type: new Abstract: Conformal prediction (CP) offers distribution-free uncertainty quantification for machine learning models, yet its interplay with fairness in downstream decision-making remains underexplored. Moving beyond CP as a standalone operation (procedural fairness), we analyze the holistic decision-making pipeline to evaluate substantive fairness-the equity of downstream…

  • PAC-Bayesian Generalization Guarantees for Fairness on Stochastic and Deterministic Classifiers

    PAC-Bayesian Generalization Guarantees for Fairness on Stochastic and Deterministic Classifiers arXiv:2602.11722v1 Announce Type: new Abstract: Classical PAC generalization bounds on the prediction risk of a classifier are insufficient to provide theoretical guarantees on fairness when the goal is to learn models balancing predictive risk and fairness constraints. We propose a PAC-Bayesian framework for deriving generalization…

  • Empirical Likelihood-Based Fairness Auditing: Distribution-Free Certification and Flagging

    Empirical Likelihood-Based Fairness Auditing: Distribution-Free Certification and Flagging arXiv:2601.20269v1 Announce Type: new Abstract: Machine learning models in high-stakes applications, such as recidivism prediction and automated personnel selection, often exhibit systematic performance disparities across sensitive subpopulations, raising critical concerns regarding algorithmic bias. Fairness auditing addresses these risks through two primary functions: certification, which verifies adherence to…

  • Double Fairness Policy Learning: Integrating Action Fairness and Outcome Fairness in Decision-making

    Double Fairness Policy Learning: Integrating Action Fairness and Outcome Fairness in Decision-making arXiv:2601.19186v1 Announce Type: new Abstract: Fairness is a central pillar of trustworthy machine learning, especially in domains where accuracy- or profit-driven optimization is insufficient. While most fairness research focuses on supervised learning, fairness in policy learning remains less explored. Because policy learning is…

  • Understanding Fairness and Prediction Error through Subspace Decomposition and Influence Analysis

    Understanding Fairness and Prediction Error through Subspace Decomposition and Influence Analysis arXiv:2510.23935v1 Announce Type: new Abstract: Machine learning models have achieved widespread success but often inherit and amplify historical biases, resulting in unfair outcomes. Traditional fairness methods typically impose constraints at the prediction level, without addressing underlying biases in data representations. In this work, we…

  • Doubly-Regressing Approach for Subgroup Fairness

    Doubly-Regressing Approach for Subgroup Fairness arXiv:2510.21091v1 Announce Type: new 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…

  • Machine Learning with Multitype Protected Attributes: Intersectional Fairness through Regularisation

    Machine Learning with Multitype Protected Attributes: Intersectional Fairness through Regularisation arXiv:2509.08163v1 Announce Type: cross Abstract: Ensuring equitable treatment (fairness) across protected attributes (such as gender or ethnicity) is a critical issue in machine learning. Most existing literature focuses on binary classification, but achieving fairness in regression tasks-such as insurance pricing or hiring score assessments-is equally…

  • Kernel-based Equalized Odds: A Quantification of Accuracy-Fairness Trade-off in Fair Representation Learning

    Kernel-based Equalized Odds: A Quantification of Accuracy-Fairness Trade-off in Fair Representation Learning arXiv:2508.15084v1 Announce Type: new Abstract: This paper introduces a novel kernel-based formulation of the Equalized Odds (EO) criterion, denoted as $EO_k$, for fair representation learning (FRL) in supervised settings. The central goal of FRL is to mitigate discrimination regarding a sensitive attribute $S$…

  • Optimal Transport Learning: Balancing Value Optimization and Fairness in Individualized Treatment Rules

    Optimal Transport Learning: Balancing Value Optimization and Fairness in Individualized Treatment Rules arXiv:2507.23349v1 Announce Type: new Abstract: Individualized treatment rules (ITRs) have gained significant attention due to their wide-ranging applications in fields such as precision medicine, ridesharing, and advertising recommendations. However, when ITRs are influenced by sensitive attributes such as race, gender, or age, they…

  • Incorporating Fairness Constraints into Archetypal Analysis

    Incorporating Fairness Constraints into Archetypal Analysis arXiv:2507.12021v1 Announce Type: new Abstract: Archetypal Analysis (AA) is an unsupervised learning method that represents data as convex combinations of extreme patterns called archetypes. While AA provides interpretable and low-dimensional representations, it can inadvertently encode sensitive attributes, leading to fairness concerns. In this work, we propose Fair Archetypal Analysis…

  • Fairness Pruning: Precision Surgery to Reduce Bias in LLMs

    Fairness Pruning: Precision Surgery to Reduce Bias in LLMs From unjustified shootings to neutral stories: how to fix toxic narratives with selective pruning The post Fairness Pruning: Precision Surgery to Reduce Bias in LLMs appeared first on Towards Data Science. Pere Martra Go to original source

  • Fairness-aware Bayes optimal functional classification

    Fairness-aware Bayes optimal functional classification arXiv:2505.09471v1 Announce Type: new Abstract: Algorithmic fairness has become a central topic in machine learning, and mitigating disparities across different subpopulations has emerged as a rapidly growing research area. In this paper, we systematically study the classification of functional data under fairness constraints, ensuring the disparity level of the classifier…

  • Discrimination-free Insurance Pricing with Privatized Sensitive Attributes

    Discrimination-free Insurance Pricing with Privatized Sensitive Attributes arXiv:2504.11775v1 Announce Type: new Abstract: Fairness has emerged as a critical consideration in the landscape of machine learning algorithms, particularly as AI continues to transform decision-making across societal domains. To ensure that these algorithms are free from bias and do not discriminate against individuals based on sensitive attributes…