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.
arXiv:2604. 16610v2 Announce Type: replace-cross Abstract: Machine learning models often inherit biases from historical data, raising critical concerns about fairness and accountability.
This PhD thesis examines how machine learning (ML) influences society, noting that ML increasingly shapes consequential decisions and recommendations. It highlights the risk of discriminatory effects when fairness is not explicitly considered in data‑driven systems. The work proposes methods for measuring fairness, decomposing ML systems to anticipate bias, and implementing interventions that reduce discrimination while preserving utility, and it outlines future research directions as ML, including generative AI, becomes more integrated into society.
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?
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...
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:2605. 05882v2 Announce Type: replace-cross Abstract: Artificial-intelligence systems are becoming ubiquitous in society, yet their predictions typically inherit biases with respect to protected attributes such as race, gender, or age.
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.
arXiv:2508. 08337v3 Announce Type: replace-cross Abstract: Algorithmic fairness research has largely framed unfairness as discrimination along sensitive attributes.
arXiv:2509. 22907v2 Announce Type: replace Abstract: Conformal Prediction (CP) is a widely used technique for quantifying uncertainty in machine learning models.
The article discusses how social networks influence decision-making and opportunity distribution, noting that network-generating mechanisms often mirror existing inequalities and can amplify disparities when used in technology. It identifies ten network effects that bias the link between intended measurements and observed data, using academic hiring as a case study to show that network biases can be neither inherently harmful nor beneficial. The authors argue for a comprehensive, networked fairness framework that incorporates both distributive and procedural justice and involves all stakeholders.
Despite groundbreaking advancements in generative models during the last decade, concerns about their lack of fairness, reinforcing societal inequalities and harming marginalized groups, remain under-...
The paper argues that fairness failures in generative models arise mainly from inadequate evaluation practices, making fairness findings hard to compare or use for deployment. It diagnoses common empirical and conceptual shortcomings in current methods and calls for a move toward standardized, generative‑specific evaluation. The authors introduce Fairness Cards, a minimal reporting artifact that explicitly documents evaluation choices—such as prompt families, counterfactual protocols, metrics, and refusal handling—to improve reproducibility, comparability, and accountability.