arXiv Machine Learning June 19, 2026 By Bruno Scarone, Alfredo Viola, Ren\'ee J. Miller

Data Bias Mitigation under Coverage Constraints & The Price of Fairness

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arXiv:2606. 20461v1 Announce Type: new Abstract: Machine learning models have been shown to exhibit discriminatory outcomes or degraded performance for individuals at the intersection of multiple sensitive attributes, such as race and gender.

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arXiv Machine Learning

Jul 14

Fairness Constraints in High-Dimensional Generalized Linear Models

arXiv:2604. 16610v2 Announce Type: replace-cross Abstract: Machine learning models often inherit biases from historical data, raising critical concerns about fairness and accountability.

By Yixiao Lin, James Booth

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arXiv AI

Jun 2

Demystifying the Optimal Fair Classifier in Multi-Class Classification

arXiv:2606. 00656v1 Announce Type: cross Abstract: Ensuring fair and equitable treatment across diverse groups, particularly in multi-class classification tasks, poses a significant challenge due to the persistent biases inherent in machine learning models.

By Li Zhang, Yuyuan Li, XiaoHua Feng, Jiaming Zhang, Fengyuan Yu, Chaochao Chen

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arXiv AI

3d ago

Efficient Active Auditing of Multi-Group Fairness with Bias Probes

arXiv:2609.40034v1 Announce Type: cross Abstract: Over the past decade, Machine Learning (ML) has been trained under dual objectives: minimizing prediction error via Empirical Risk Minimization (ERM)...

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FairLint-DL: An IDE-Native Tool for Fairness Debugging of Deep Learning Software

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arXiv Machine Learning

Jun 2

Beyond Procedure: Substantive Fairness in Conformal Prediction

arXiv:2602. 16794v2 Announce Type: replace-cross Abstract: Conformal prediction (CP) offers distribution-free uncertainty quantification for machine learning models, yet its interplay with fairness in downstream decision-making remains underexplored.

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arXiv AI

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No-Free-Fairness: Fundamental Limits and Trade-offs in Learning Systems

arXiv:2606. 17810v1 Announce Type: cross Abstract: In this paper, we establish a set of theoretical impossibility results, termed the No-Free-Fairness theorems, that identify three fundamental sources of disparity in learning systems.

By Khoat Than

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Fairness Constraints in High-Dimensional Generalized Linear Models

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