arXiv Machine Learning June 29, 2026 By Maaya Sakata, Kazuto Fukuchi

Fair Classification with Efficient and Post-hoc Controllable Fairness-Accuracy Trade-off

Read the original on arXiv Machine Learning →

arXiv:2606. 28097v1 Announce Type: new Abstract: Post-hoc controllability of fair machine learning models, the ability to control the trade-off between fairness and accuracy after training, is valuable for practical deployment.

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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

fine-tuning safety

arXiv AI

Aug 17

Training Fair Tabular Foundation Models

arXiv:2608. 14211v1 Announce Type: cross Abstract: Tabular Foundation Models (TFMs) have emerged as leading methods for tabular predictive tasks, leveraging in-context learning to predict on new data without task-specific training.

By Patrik Kenfack, Jesse C. Cresswell, Anthony L. Caterini, Samira Ebrahimi Kahou, Ulrich A\"ivodji

llms safety

arXiv Computer Vision

Aug 25

Fairness-Aware Low-Rank Representation Fine-Tuning

arXiv:2503.05684v2 Announce Type: replace-cross Abstract: Pre-trained foundation models can be efficiently adapted for specific tasks using Low-Rank Adaptation (LoRA), but the fairness properties of...

By Parameswaran Kamalaruban, Mark Anderson, Stuart Burrell, Maeve Madigan, Piotr Skalski, David Sutton

fine-tuning safety

arXiv Machine Learning

Sep 23

FairMean: Promoting Fairness in Distributed Learning under Label Poisoning Attacks

FairMean is a new approach for distributed learning that addresses the conflict between fairness and robustness to label poisoning attacks. It assigns weights to client gradients based on a bounded, nondecreasing function of local loss, giving higher weight to high‑loss clients to promote fairness while limiting the influence of poisoned clients. The method is shown to improve fairness compared to standard average‑loss minimization and to reduce accuracy variance while boosting worst‑client accuracy in experiments.

By Huigan Zheng, Jiaojiao Zhang, Yongxiang Liu

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arXiv Machine Learning

Jul 16

A Generalised Exponentiated Gradient Approach to Enhance Fairness in Binary and Multi-class Classification Tasks

arXiv:2603. 21393v2 Announce Type: replace Abstract: The widespread use of AI and ML models in sensitive areas raises significant concerns about fairness.

By Maryam Boubekraoui, Giordano d'Aloisio, Antinisca Di Marco

safety

arXiv Machine Learning

Sep 1

RL-FAT: Reinforcement Learning for Fair Adversarial Training

arXiv:2608.29247v1 Announce Type: new Abstract: Deep neural networks remain highly vulnerable to adversarial perturbations, and adversarial training (AT) has become a widely used approach for improvi...

By Tejaswini Medi, Levan Mikeladze, Margret Keuper

reinforcement-learning safety

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Related stories

Demystifying the Optimal Fair Classifier in Multi-Class Classification

Training Fair Tabular Foundation Models

Fairness-Aware Low-Rank Representation Fine-Tuning

FairMean: Promoting Fairness in Distributed Learning under Label Poisoning Attacks

A Generalised Exponentiated Gradient Approach to Enhance Fairness in Binary and Multi-class Classification Tasks

RL-FAT: Reinforcement Learning for Fair Adversarial Training