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21 Yale J.L. & Tech. 98 (2019)
Artificial Intelligence-Based Suicide Prediction

handle is hein.journals/yjolt21 and id is 480 raw text is: Artificial Intelligence-Based Suicide Prediction Mason Marks* ABSTRACT Suicidal thoughts and behaviors are an international public health problem contributing to 800,000 annual deaths and up to 25 million nonfatal suicide attempts. In the United States, suicide rates have increased steadily for two decades, reaching 47,000 per year and surpassing annual motor vehicle deaths. This trend has prompted government agencies, healthcare systems, and multinational corporations to invest in artificial intelligence-based suicide prediction algorithms. This article describes these tools and the underexplored risks they pose to patients and consumers. AI-based suicide prediction is developing along two separate tracks. In medical suicide prediction, Al analyzes data from patient medical records. In social suicide prediction, Al analyzes consumer behavior derived from social media, smartphone apps, and the Internet of Things (IoT). Because medical suicide prediction occurs within the context of healthcare, it is governed by the Health Information Portability and Accountability Act (HIPAA), which protects patient privacy; the Federal Common Rule, which protects the safety of human research subjects; and general principles of medical ethics. Medical suicide prediction tools are developed methodically in compliance with these regulations, and the methods of its developers are published in peer-reviewed academic journals. In contrast, social suicide prediction typically occurs outside the healthcare system where it is almost completely unregulated. Corporations maintain their suicide prediction methods as proprietary trade secrets. Despite this lack of transparency, social suicide predictions are deployed globally to affect people's lives every day. Yet little is known about their safety or effectiveness. * Assistant Professor, Gonzaga University School of Law; Affiliated Fellow, Yale Law School Information Society Project; Doctoral Researcher, Leiden Law School Center for Law and Digital Technologies. Many thanks to Katherine Strandburg, Ann Bartow, Ari Waldman, Andrea Matwyshyn, Ido Kilovaty, Thomas Kadri, and Roger Ford for their helpful comments on an earlier draft of this article. Thank you to Abbe Gluck, Jack Balkin, Katherine Kraschel, Adam Pan, Phillip Yao, the Yale Solomon Center for Health Law & Policy, and the Yale Information Society Project for the opportunity to discuss this article at the Law and Policy of AI, Robotics and Telemedicine in Health Care conference at Yale Law School. Special thanks to Joel Reidenberg, the Center on Law and Information Policy at Fordham Law School, and the Innovation Center for Law and Technology at New York Law School for the opportunity to discuss the article at the Northeast Privacy Scholars Workshop. 98

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