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: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
arXiv:2609.40034v1 Announce Type: cross
Abstract: Over the past decade, Machine Learning (ML) has been trained under dual objectives: minimizing prediction error via Empirical Risk Minimization (ERM)...
By Ayoub Ajarra, Debabrota Basu
Emerging legislation requires large language models (LLMs) to be audited for compliance with regulatory standards, particularly fairness. Such black-box audits typically assume a single auditor with a...
arXiv:2610.01005v1 Announce Type: new
Abstract: As artificial intelligence is increasingly deployed, algorithmic unfairness has raised growing concerns and intensified demands for transparent fairnes...
By Jie Tang, Chuanlong Xie, Lixing Zhu
arXiv:2606. 26200v1 Announce Type: cross Abstract: Modern machine learning systems have outgrown their origins as isolated predictive constructs, evolving into complex socio-technical architectures that actively mediate human opportunity.
By Antonio Ferrara
arXiv:2604. 16610v2 Announce Type: replace-cross Abstract: Machine learning models often inherit biases from historical data, raising critical concerns about fairness and accountability.
By Yixiao Lin, James Booth
arXiv:2602. 16794v2 Announce Type: replace-cross Abstract: Conformal prediction (CP) offers distribution-free uncertainty quantification for machine learning models, yet its interplay with fairness in downstream decision-making remains underexplored.
By Pengqi Liu, Zijun Yu, Mouloud Belbahri, Arthur Charpentier, Masoud Asgharian, Jesse C. Cresswell
The paper introduces a new framework for evaluating spatial fairness in predictive models by considering individuals’ activity spaces rather than just a single residential location. It proposes associating users with multiple geographic partitions and applying a spatial scan statistic to detect unfairness across these activity-space patterns. Experiments on synthetic datasets demonstrate the method’s effectiveness in identifying unfair treatment and retrieving affected objects, while highlighting a trade‑off in localization performance across resolutions.
By Francesco Lettich, Mario A. Nascimento, Chiara Pugliese, Chiara Renso
arXiv:2608. 00568v1 Announce Type: new Abstract: Fairness audits are increasingly mandated in high-stakes applications such as hiring, lending, and automated decision-making.
By Rachit Verma, Padala Manisha, Sujit Gujar
arXiv:2507. 20708v3 Announce Type: replace Abstract: The rapid deployment of AI systems in high-stakes domains, including those classified as high-risk under the The EU AI Act (Regulation (EU) 2024/1689), has intensified the need for reliable compliance auditing.
By Valentin Lafargue, Adriana Laurindo Monteiro, Emmanuelle Claeys, Laurent Risser, Jean-Michel Loubes
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