arXiv Machine Learning June 9, 2026 By Guangya Cai

Generalizing Fair Top-$k$ Selection: An Integrative Approach

Read the original on arXiv Machine Learning →

arXiv:2603. 04689v3 Announce Type: replace-cross Abstract: Fair top-$k$ selection, which ensures appropriate proportional representation of members from minority or historically disadvantaged groups among the top-$k$ selected candidates, has drawn significant attention.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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arXiv AI

Jun 29

Unbiased Binning for Fairness-aware Attribute Representation

arXiv:2509. 21785v2 Announce Type: replace-cross Abstract: Discretizing raw features into bucketized attribute representations is a popular step before sharing a dataset.

By Abolfazl Asudeh, Zeinab Asoodeh, Bita Asoodeh, Omid Asudeh

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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

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arXiv Machine Learning

Jul 21

COVAriance-Induced Fairness Gap Penalty for Subgroup-Fair Clustering

arXiv:2607. 18119v1 Announce Type: cross Abstract: Fair clustering aims to make cluster assignments independent of sensitive attributes, but this goal becomes challenging when multiple sensitive attributes jointly define many subgroups.

By Kyungseon Lee, Hankyo Jeong, Kunwoong Kim, Kwanho Lee, Yongdai Kim

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arXiv AI

Jun 17

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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arXiv Machine Learning

Aug 10

Fairis: Fairness-Aware Aggregation with Provable Influence Containment against Fairness Poisoning Attacks in Collaborative Machine Learning

arXiv:2608. 06469v1 Announce Type: cross Abstract: Collaborative machine learning among financial institutions must be both group-fair and robust against deliberate adversarial manipulation.

By Devharsh Trivedi, Nesrine Kaaniche, Nikos Triandopoulos, Maryline Laurent, Jackson Walters

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arXiv Machine Learning

Aug 11

Fairness in Link Prediction Beyond Demographic Parity: A Reproducibility Study

arXiv:2608. 09899v1 Announce Type: new Abstract: In fair ranked link prediction, demographic parity ($\Delta_\mathrm{DP}$) is a common fairness metric.

By Valentijn Oldenburg, Floris de Kam, Stef de Wildt, Jarno Nilson Balk

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Related stories

Unbiased Binning for Fairness-aware Attribute Representation

Data Bias Mitigation under Coverage Constraints & The Price of Fairness

COVAriance-Induced Fairness Gap Penalty for Subgroup-Fair Clustering

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

Fairis: Fairness-Aware Aggregation with Provable Influence Containment against Fairness Poisoning Attacks in Collaborative Machine Learning

Fairness in Link Prediction Beyond Demographic Parity: A Reproducibility Study