arXiv Machine Learning June 17, 2026 By Jiwoo Han, Moulinath Banerjee, Yuekai Sun

Maximin Relative Improvement: Fair Learning as a Bargaining Problem

Read the original on arXiv Machine Learning →

arXiv:2602. 04155v2 Announce Type: replace-cross Abstract: When deploying a single predictor across multiple subpopulations, we propose a fundamentally different approach: interpreting group fairness as a bargaining problem among subpopulations.

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 Machine Learning.

safety

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

safety

arXiv AI

4d ago

A Comprehensive View of Fairness through Distributional Stability

arXiv:2609.37061v1 Announce Type: cross Abstract: We view fairness as a property of distributional stability. Rather than assessing a predictor under a fixed data distribution, we study how its predi...

By Gayane Taturyan, Charlotte Laclau, Stephan Cl\'emencon

benchmarks safety

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

safety

arXiv Machine Learning

Jun 16

Multi-User Dueling Bandits: A Fair Approach using Nash Social Welfare

arXiv:2605. 01961v2 Announce Type: replace Abstract: Learning from human preference data is becoming a useful tool, from fine-tuning large language models to training reinforcement learning agents.

By Maheed H. Ahmed, Mahsa Ghasemi

llms agents reinforcement-learning fine-tuning safety

arXiv Machine Learning

Jun 19

Matching Markets meet Cumulative Prospect Theory: Towards Optimal and Adversarially Robust Learning

arXiv:2606. 19883v1 Announce Type: new Abstract: We study a multi-agent multi-armed bandit problem in the competitive setup with two-sided matching markets under a human centric decision making model.

By Ananya Kunisetty, Avishek Ghosh

agents reinforcement-learning benchmarks safety

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

robotics benchmarks safety

One email a morning, machine-written

Related stories

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

A Comprehensive View of Fairness through Distributional Stability

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

Multi-User Dueling Bandits: A Fair Approach using Nash Social Welfare

Matching Markets meet Cumulative Prospect Theory: Towards Optimal and Adversarially Robust Learning

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