Tempered enthusiasm by interviewed experts for synthetic data and ELSI checklists for AI in medicine

作     者：Laura Y Cabrera Jennifer Wagner Sara Gerke Daniel Susser 

作者机构：Department of Engineering Science and Mechanics Pennsylvania State University W-316 Millennium Science Complex University Park PA 16802 USA. Department of Philosophy Pennsylvania State University University Park PA 16802 USA. Huck Institutes of the Life Sciences Pennsylvania State University University Park PA 16802 USA. Rock Ethics Institute Pennsylvania State University University Park PA 16802 USA. Bioethics Program Pennsylvania State University University Park PA 16802 USA. School of Engineering Design and Innovation Pennsylvania State University University Park PA 16802 USA. Department of Anthropology Pennsylvania State University University Park PA 16802 USA. Department of Biomedical Engineering Pennsylvania State University University Park PA 16802 USA. Institute for Computational and Data Sciences Pennsylvania State University University Park PA 16802 USA. College of Law and European Union Center University of Illinois Urbana-Champaign Champaign IL 61820 USA. Department of Information Science Cornell University Ithaca NY 14853 USA. 

出 版 物：《AI and ethics》 

年 卷 期：2025年第5卷第3期

页      面：3241-3254页

主　　题：Artificial intelligence Computational checklists ELSI Synthetic data 

摘      要：Synthetic data are increasingly being used in data-driven fields. While synthetic data is a promising tool in medicine, it raises new ethical, legal, and social implications (ELSI) challenges. There is a recognized need for well-designed approaches and standards for documenting and communicating relevant information about artificial intelligence (AI) research datasets and models, including consideration of the many ELSI challenges. This study investigates the ethical dimensions of synthetic data and explores the utility and challenges of ELSI-focused computational checklists for biomedical AI via semi-structure interviews with subject matter experts. Our results suggest that AI experts have tempered views about the promises and challenges of both synthetic data and ELSI-focused computational checklists. Experts discussed a number of ELSI issues covered by previous literature on the topic, such as issues of bias and privacy, yet other less discussed ELSI issues, such as social justice implications and issues of trust were also raised. When discussing ELSI-focused computational checklists our participants highlighted the challenges connected to developing and implementing them.