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.
By Joachim Baumann
arXiv:2608. 13618v1 Announce Type: new Abstract: A single clinical algorithm can deliver unequal accuracy across patient groups, and concern about such disparity has grown as artificial intelligence (AI) spreads through clinical decision-making.
By Shujie Luan, Shubhranshu Singh, Tinglong Dai
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.
By Antonio Ferrara
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:2608.13618v2 Announce Type: replace
Abstract: Clinical algorithms can perform differently across patient groups, a concern that grows as these algorithms increasingly take the form of artificia...
By Shujie Luan, Shubhranshu Singh, Tinglong Dai
arXiv:2606. 11214v1 Announce Type: cross Abstract: Algorithmic fairness is essential for responsible ML-driven public health research, yet its practical implementation remains limited.
By Sara Altamirano, Tijs Portegies, Sennay Ghebreab
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?
By Youmi Suk
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.
By Nick Souligne, Vignesh Subbian
arXiv:2608. 07477v1 Announce Type: cross Abstract: This thesis examines the fairness of Automated Machine Learning (AutoML) tools in human resource hiring systems through the combined lenses of regulation, business strategy, and Human-Computer Interaction (HCI).
By Sundaraparipurnan Narayanan
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.
By Nick Souligne, Isabella Mixton-Garcia, Vignesh Subbian
arXiv:2606. 00044v1 Announce Type: cross Abstract: The integration of artificial intelligence into clinical medicine creates a fundamental tension between algorithmic probabilistic reasoning and the experiential intuition of expert physicians; applying Lawrence Lessig's \enquote{Code is Law} framework, I argue that the architecture of clinical AI systems already functions as de facto medical regulation, reshaping liability and the standard of care.
By Aizierjiang Aiersilan