The paper introduces a targeted learning approach to assess fairness in the data generating process used for training predictive models, rather than focusing solely on model performance. It derives estimators for demographic parity, equal opportunity, and conditional mutual information, and demonstrates through simulations that these estimators improve confidence‑interval coverage and reduce false detections of population unfairness compared to fixed‑model inference. The authors also apply the method to real data to illustrate its practical utility.
By Joseph H. Rudoler, Alexander Asemota, Giles Hooker
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.
By Bruno Scarone, Alfredo Viola, Ren\'ee J. Miller
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
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
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...
By Francesca Panero, Ernst C. Wit, Marco Scutari
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
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.
By Thomas Souverain, Paul \'Egr\'e
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
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.
By Abdullah Al Noman, Fahmid Al Rifat, Tahrima Hashem, Syed Muhammad Ibne Zulfiker, Rishov Paul, Tanzima HAshem
arXiv:2606. 01719v1 Announce Type: cross Abstract: Machine learning models trained on sensitive data can inadvertently leak population-level information about their training distributions -- a threat known as distribution inference attack (DIA).
By Rakshit Naidu
arXiv:2608.02551v2 Announce Type: replace-cross
Abstract: Fairness evaluation concerns not only what a model produces, but also what its outputs ought to be compared against. When a model generates "...
By Zeshen Zheng, Yujia He, Qianmian Lin, Xiangyue Huang, Wenqing Chen
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