arXiv:2609.39025v1 Announce Type: cross
Abstract: Fairness assessment in algorithmic decisions that affect individuals, such as credit scoring, often relies on parity measures calculated at the aggre...
By Dalia Atif, Paolo Giudici
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:2608.24582v1 Announce Type: cross
Abstract: Credit risk models increasingly need to combine predictive accuracy with transparent explanations and auditable fairness constraints. Logistic regres...
By Victor Medina-Olivares, Stefan Lessmann, Jonathan Crook
The paper argues that traditional probabilistic fairness metrics can miss significant disparities in the actual consequences of decisions. By introducing a utility-based framework, the authors show that a process can satisfy ε-fairness yet still be maximally unfair when utilities are considered. They apply this framework to college admissions and credit‑risk assessment, demonstrating that equalizing probabilities alone may mask unequal utility outcomes across groups.
By Tolulope Fadina, Thorsten Schmidt
Credit risk models increasingly need to combine predictive accuracy with transparent explanations and auditable fairness constraints. Logistic regression remains attractive because its coefficients ar...
arXiv:2604. 04241v2 Announce Type: replace Abstract: Risk scoring systems are widely used in high-stakes domains to assist decision-making.
By Wenhao Chi, \c{S}. \.Ilker Birbil