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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*
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
* 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.


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