The paper introduces a penalized fair regression framework that incorporates unfairness penalties for multiple groups, specifically targeting binary outcomes with true positive rate disparity penalties. It can be efficiently implemented via a reduction to cost‑sensitive classification and includes new score functions for selecting penalty weights automatically. Empirical studies show that the method achieves a better fairness‑accuracy trade‑off than existing approaches, and when applied to a national chronic kidney disease dataset it markedly improves fairness across race and ethnicity groups without significantly harming overall model fit.
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.
The paper examines how fairness conclusions in ICU mortality prediction using MIMIC-IV depend on the choice of metrics and the granularity of demographic analysis. It compares predictive-utility and subgroup-error metrics across various fairness interventions and introduces a lightweight adaptation strategy that balances ethnicity, gender, and insurance representation without conditioning on mortality outcomes. The study finds that different interventions can be evaluated differently across accuracy, sensitivity, and false-positive rate, and that marginal demographic summaries may hide heterogeneous error patterns within intersectional subgroups.
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.
arXiv:2609.07959v1 Announce Type: cross Abstract: Machine learning models are widely used in clinical applications, social media, law enforcement and critical infrastructure. Verifying whether their...
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.
arXiv:2604. 16610v2 Announce Type: replace-cross Abstract: Machine learning models often inherit biases from historical data, raising critical concerns about fairness and accountability.
arXiv:2603. 21393v2 Announce Type: replace Abstract: The widespread use of AI and ML models in sensitive areas raises significant concerns about fairness.
arXiv:2407. 14766v4 Announce Type: replace-cross Abstract: This paper presents a philosophical and experimental study of fairness interventions in AI classification, centered on the explainability and transparency of corrective methods, and on the opposition between two fairness criteria, namely Demographic Parity and Equalized Odds.
The paper examines how multi‑level fairness techniques—combining several bias‑mitigation steps—can reduce biases across patient demographics in health informatics. It reviews current literature, identifies gaps in implementation and reporting of health equity outcomes, and evaluates the role of reporting standards such as MINIMAR and TRIPOD in enhancing transparency. The authors conclude with recommendations to improve reporting transparency, broaden adoption of multi‑level fairness methods, and explicitly prioritize health equity in future research.
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.
arXiv:2604. 16450v2 Announce Type: replace-cross Abstract: Intersectional biases in healthcare data can produce compound disparities in clinical machine learning models, yet most fairness evaluations assess demographic attributes independently.
The paper presents a method to estimate the Bayes optimal fairness‑accuracy tradeoff using only soft labels and group information. It derives an explicit expression for the fair Bayes error rate and constructs consistent, instance‑free estimators with provable bias bounds and sample complexity guarantees. Empirical results show that the estimators provide a robust, low‑variance estimate of the optimal fairness‑accuracy Pareto frontier even when soft labels are noisy.