arXiv AI June 17, 2026 By Khoat Than

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.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

safety

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

fine-tuning safety

arXiv Machine Learning

Jun 8

Learning Fair Demand Models

arXiv:2606. 06830v1 Announce Type: cross Abstract: Data-driven pricing is increasingly prevalent in sectors such as airlines, lending, insurance, and retail.

By Adam N. Elmachtoub, Hyemi Kim, Jonathan Y. Tan

safety

arXiv Machine Learning

Jun 19

Data Bias Mitigation under Coverage Constraints & The Price of Fairness

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.

By Bruno Scarone, Alfredo Viola, Ren\'ee J. Miller

safety

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.

By Pengqi Liu, Zijun Yu, Mouloud Belbahri, Arthur Charpentier, Masoud Asgharian, Jesse C. Cresswell

llms safety

arXiv Machine Learning

Aug 3

End-to-End Fairness Optimization with Fair Decision-Focused Learning

arXiv:2607. 29441v1 Announce Type: new Abstract: Many real-world systems rely on predictive models to inform decisions, and fairness concerns arise in both the prediction and decision stages.

By Yu Wang (Xinying), Violet (Xinying), Chen

safety

arXiv Machine Learning

Sep 15

Unfair Utilities and First Steps Towards Improving Them

arXiv:2306.00636v3 Announce Type: replace-cross Abstract: Many fairness criteria constrain the policy or choice of predictors, which can have unwanted consequences, in particular, when optimizing the...

By Frederik Hytting J{\o}rgensen, Sebastian Weichwald, Jonas Peters

safety

One email a morning, machine-written

Related stories

Demystifying the Optimal Fair Classifier in Multi-Class Classification

Learning Fair Demand Models

Data Bias Mitigation under Coverage Constraints & The Price of Fairness

Beyond Procedure: Substantive Fairness in Conformal Prediction

End-to-End Fairness Optimization with Fair Decision-Focused Learning

Unfair Utilities and First Steps Towards Improving Them