arXiv Machine Learning
5d ago

Individual Fairness via Local Predictive Consistency

The paper introduces a node‑level relaxation of individual fairness called local predictive consistency, where each prediction is compared to a similarity‑weighted average of predictions in its local neighborhood. This approach yields an interpretable fairness score and a regularized learning method based on local residuals, and it explicitly constructs the neighborhood graph with threshold selection guided by graph‑coverage diagnostics. The authors apply the method to binary classification, regression, and time‑to‑event prediction, and compare it with pairwise constraint‑based fairness formulations to analyze fairness‑performance trade‑offs.

By Zhouting Zhao, Tin Lok James Ng
arXiv Machine Learning
Oct 8

Robust Decentralized Fairness Auditing

arXiv:2610.10199v1 Announce Type: new Abstract: Emerging legislation requires large language models (LLMs) to be audited for compliance with regulatory standards, particularly fairness. Such black-bo...

By Sayan Biswas, Jade Garcia Bourr\'ee, Anne-Marie Kermarrec, Palak, Martijn de Vos