arXiv Machine Learning

Robust Mixture Models for Algorithmic Fairness Under Latent Heterogeneity

The paper introduces ROME, a framework that learns latent group structure while optimizing worst-group predictive performance. ROME links latent-variable modeling with distributionally robust optimization through an Expectation-Maximization approach for linear models and a neural Mixture-of-Experts for nonlinear settings. Experiments on simulations and three real-world regression datasets show that ROME improves worst-group performance while maintaining competitive overall accuracy compared to existing group-aware and group-label-free robust learning methods.

arXiv AI

Aug 25

Fairness-Aware Mixture-of-Experts via Subgroup Reweighting and Gate Regularization

The paper proposes a fairness-aware Mixture-of-Experts (MoE) framework that tackles routing-induced bias by applying subgroup reweighting to correct data imbalance and gate entropy regularization to prevent the gating network from collapsing onto subgroup attributes. This end-to-end approach keeps expert utilization balanced and interpretable, offering a clear view of how subgroups are allocated across experts. Experiments show that the method improves fairness while maintaining competitive predictive performance.

By Sunhee Hwang

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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

benchmarks safety

arXiv Machine Learning

Jun 4

UniFair: A unified fair clustering approach based on separation and compactness

arXiv:2606. 04777v1 Announce Type: new Abstract: Clustering is increasingly used to support high-impact decisions, yet standard objectives such as $k$-means can produce clusterings that treat demographic groups unequally.

By Antonia Karra, Vasiliki Papanikou, Georgios Vardakas, Evaggelia Pitoura, Aristidis Likas

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arXiv Machine Learning

Jun 9

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

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.

By Guangya Cai

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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 13

FairSelect: A Systematic Evaluation of Multi-Level and Intersectional Algorithmic Fairness

arXiv:2607. 08953v1 Announce Type: new Abstract: Algorithmic fairness methods are increasingly used to identify and mitigate bias in machine learning models, yet most approaches are evaluated in isolation and along single demographic axes.

By Nick Souligne, Isabella Mixton-Garcia, Vignesh Subbian

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arXiv Machine Learning

2d ago

Rank-Constrained Adaptation for Reliable Real-World Performance

arXiv:2602.06924v3 Announce Type: replace Abstract: Deep learning models trained to optimize average accuracy often exhibit systematic failures on particular subpopulations. In real-world settings li...

By Abinitha Gourabathina, Hyewon Jeong, Teya Bergamaschi, Marzyeh Ghassemi, Collin Stultz

arXiv Machine Learning

Aug 12

When Interpretability Is Unequally Distributed: Fairness in Hybrid Interpretable Models

arXiv:2605. 28626v2 Announce Type: replace Abstract: Hybrid interpretable models combine a transparent component with a black-box model by assigning some examples to the former and deferring the rest to the latter.

By Ziba Jabbar Zare, Ulrich A\"ivodji, Julien Ferry, Thibaut Vidal

efficiency benchmarks safety

arXiv AI

Sep 10

Discovering Latent Groups for Robust Classification

arXiv:2606.23609v2 Announce Type: replace-cross Abstract: Machine learning models exploit spurious correlations, achieving high average accuracy but failing disproportionately on underrepresented sub...

By Ankur Garg, Ulrich A\"ivodji, Samira Ebrahimi Kahou, Vincent Michalski

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Hugging Face Trending Papers

Jul 9

FairSelect: A Systematic Evaluation of Multi-Level and Intersectional Algorithmic Fairness

Algorithmic fairness methods are increasingly used to identify and mitigate bias in machine learning models, yet most approaches are evaluated in isolation and along single demographic axes. This limits practical guidance for selecting fairness strategies, where disparities may arise across intersectional subgroups and across multiple stages of the modeling lifecycle.

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arXiv Computation and Language

Sep 1

AIA$^{2}$: Attribute-Agnostic Imbalance Augmentation for Subgroup Robustness

arXiv:2608.30297v1 Announce Type: new Abstract: Attributes describing data content and context can induce diverse imbalance patterns that go beyond label imbalance alone. However, existing studies pr...

By Hanshu Rao, Guangzeng Han, Xiaolei Huang

llms nlp benchmarks

arXiv Machine Learning

Jun 2

FedCF: Fair Federated Conformal Prediction

arXiv:2509. 22907v2 Announce Type: replace Abstract: Conformal Prediction (CP) is a widely used technique for quantifying uncertainty in machine learning models.

By Anutam Srinivasan, Aditya T. Vadlamani, Amin Meghrazi, Srinivasan Parthasarathy

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