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.
arXiv:2306.00636v3 Announce Type: replace-cross Abstract: Many fairness criteria constrain the policy or choice of predictors, which can have unwanted consequences, in particular, when optimizing the...
arXiv:2402. 01811v2 Announce Type: replace Abstract: Credit scoring has been catalogued by the European Commission and the Executive Office of the US President as a high-risk classification task, in light of the potential harms of making loan approval decisions based on models that would be biased against certain groups.
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...
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.
arXiv:2607. 19389v1 Announce Type: cross Abstract: As AI-driven Decision Makers (ADMs) influence our socioeconomic reality, their roles in both enhancing efficiency and amplifying the social biases have drawn attention.
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.
The paper introduces the AR Fairness Metamodel, a structured framework for representing, analyzing, and comparing fairness scenarios. It incorporates key elements such as agents, resources, and their attributes, and supports both discrete and continuous fairness measures—including equality, equity, group fairness, individual fairness, the Gini index, the Theil index, Jain's fairness index, and a specific measure for Australia's Child Care Subsidy. The metamodel builds on the Tiles framework, offering modular components that can be connected to capture diverse fairness definitions, and includes formal proofs of relationships among group fairness, individual fairness, and envy‑freeness. An open‑source implementation of the Tiles framework is provided to facilitate practical fairness modeling and evaluation across various applications.
arXiv:2601. 10600v2 Announce Type: replace-cross Abstract: In the context of multi-agent multi-armed bandits (MA-MAB), fairness is often reduced to outcomes: maximizing welfare, reducing inequality, or balancing utilities.
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.
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.
arXiv:2608. 03917v1 Announce Type: new Abstract: Causal perception occurs when agents with competing Structural Causal Models (SCMs) of the same system infer different probability distributions, including the hypothetical distributions implied by each agent's SCM under the same set of interventions.
arXiv:2607. 17679v1 Announce Type: cross Abstract: This is an invited commentary on the Psychometrika focus article "Fairness Issues and Evaluation in Psychometrics and AI/ML: What Can We Learn from Each Field?