arXiv AI October 31, 2026 By Jennifer Xu, Tamara Babaian

Algorithmic Bias from the Perspectives of Healthcare Professionals

The paper examines algorithmic bias in machine learning and AI used in healthcare information systems. Using survey and interview data from healthcare professionals in varied roles, it explores how perceptions of bias, fairness, and acceptance differ by job function and algorithm characteristics. The findings highlight the need for further research to ensure fairness and equality in healthcare AI applications.

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arXiv Machine Learning

Sep 11

On the Societal Impact of Machine Learning

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

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arXiv AI

Aug 17

Algorithm Design and Physician Liability

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 AI

Jun 26

Statistical and Structural Approaches to Algorithmic Fairness

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

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arXiv Machine Learning

Sep 10

The Role of Uncertainty in Assessing the Fairness of Machine Learning Models

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

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arXiv AI

Oct 15

AI Model Design and Physician Liability

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 AI

Jun 11

From Awareness to Action: Understanding and Overcoming the Research-Practice Gap in Algorithmic Fairness for Public Health

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

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