Regulatory Foundations, Ethics, and Law Program

Human Participants in AI Research: Ethics and Transparency in Practice

This Institute of Electrical and Electronics Engineers (IEEE) article emphasizes the ethical considerations when incorporating human participants into AI studies. The author discusses the importance of transparency in data collection and analysis, ensuring informed consent, and addressing biases in data that could impact AI system outcomes. These principles are crucial to maintaining the integrity of research involving human participants, especially as AI technologies become more embedded in decision-making processes across fields like healthcare and social services​.

Author: Kevin McKee

FDA Perspective on the Regulation of Artificial Intelligence in Health Care and Biomedicine

The article from the Journal of the American Medical Association (JAMA) discusses the FDA’s perspective on regulating AI in healthcare, focusing on its application in clinical settings and biomedical research. This is crucial for human participant research, because it addresses the safety, efficacy, and ethical concerns around AI tools used in trials, diagnostics, and treatment. Ensuring AI compliance with FDA standards helps safeguard participants and ensures that AI technologies are deployed responsibly in clinical research environments.

Authors: Haider J. Warraich,  Troy Tazbaz, and  Robert M. Califf

Medicine’s Lessons for AI Regulation 

This article from The New England Journal of Medicine discusses the need for contemporary strategies in informed consent to ensure that participants are genuinely informed and able to make autonomous decisions, addressing both ethical and practical aspects of modern research practices. This article emphasizes the need for accurate data interpretation and responsible communication, especially in research involving AI. It underscores the importance of rigorous data analysis and transparency in research involving human participants.

Author: Laura Stark

The Current State of Artificial Intelligence Generative Language Models is More Creative Than Humans on Divergent Thinking Tasks

This article from Scientific Reports explores how biases among researchers can influence study outcomes, emphasizing the need for strategies to minimize these biases to enhance the validity and reliability of clinical trials. This article demonstrates how the use of AI and other research tools can lead to a more comprehensive understanding of an issue.

Authors: Kent F. Hubert, Kim N. Awa, and Darya L. Zabelina

Research Handbook on Health, AI and the Law

This article from Elgar Online provides a comprehensive examination of ethical standards and regulatory frameworks across different jurisdictions, offering insights into best practices and fostering improved protection for research participants globally. This article is significant because it explores the intersection of technology and ethics in the context of research. It delves into the ethical and legal considerations of using AI technologies in studies involving human participants, including issues of consent, privacy, and data security.

Authors: Barry Solaiman and Glenn Cohen

AI and the Falling Sky: Interrogating X- Risk [PDF]

This article from the Journal of Medical Ethics addresses contemporary ethical challenges and considerations in human participant research, offering valuable insights into evolving norms and practices that enhance the protection and respect for participants in the research process. The article contributes to a deeper understanding of the potential long-term impacts of AI, guiding both research practices and the responsible development of AI technologies.

Authors: Nancy S. Jecker, Jean- Christophe Bélisle- Pipon, Caesar Alimsinya Atuire, Vardit Ravitsky, and Anita Ho

Disparities in Clinical Studies of AI Enabled Applications from a Global Perspective

This article from Nature focuses on the application of artificial intelligence (AI) to predict cardiovascular disease risk using electronic health records (EHRs). It highlights the transformative potential of AI in improving cardiovascular risk prediction and patient care, while also emphasizing the need for careful management of ethical and privacy concerns in research involving human participants.

Authors: Rui Yang, Sabarinath Vinod Nair, Yuhe Ke, Danny D’Agostino, Mingxuan Liu, Yilin Ning, and Nan Liu