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 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.
By Jennifer Xu, Tamara Babaian
arXiv:2608.29478v1 Announce Type: cross
Abstract: Scholarly work which aims to describe potential societal impacts (e.g., risks) of proliferating technology (especially related to artificial intellig...
By Kyra Wilson, Sabrina Kang, Saloni Dash, Aylin Caliskan
arXiv:2607. 12588v1 Announce Type: new Abstract: According to the recent European legislation, high-risk AI systems will have to adapt in order to comply with requirements related to specific areas, like risk management, data quality and governance, logging and traceability, technical documentation, transparency, human oversight, and accuracy, as outlined in the European Artificial Intelligence (AI) Act.
By Anna Gatzioura, Vrettos Moulos, Nina Baranowska
arXiv:2606. 04971v1 Announce Type: new Abstract: Machine learning engineering (MLE) agents promise to automate end-to-end ML pipeline development from raw data and natural language instructions, potentially making ML accessible to non-technical domain experts.
By Anna Richter, Julia Stoyanovich, Sebastian Schelter
arXiv:2604. 27011v2 Announce Type: replace-cross Abstract: AutoML, intended as the process of automating the application of machine learning to real-world problems, is a key step for AI popularisation.
By Alessia Berarducci, Eric Rossetto, Alessandro Antonucci, Marco Zaffalon