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Evaluation ViewAttendees 10

Panel Discussions

Technology/Digital Health

Panel Discussion 19 - Prometheus and the Machine: Ethical Implications for Emerging Field of Artificial Intelligence in Behavioral Healthcare

Saturday, November 16, 2024
10:30 AM – 12:00 PM EST
Location: Liberty B, Level 3, Downtown Marriott
Earn 1.5 Credit
Keywords: Technology / Mobile Health, Ethics, Implementation
Recommended Readings: Stade, E. C., Stirman, S. W., Ungar, L. H., Boland, C. L., Schwartz, H. A., Yaden, D. B., … Eichstaedt, j. C. (2023, April 24). Large language models could change the future of behavioral healthcare:  A proposal for responsible development and evaluation. https://doi.org/10.31234/osf.io/cuzvr,  
Creed, T. A., Salama, L., Slevin, R., Tanana, M., Imel, Z., Narayanan, S., & Atkins, D. C. (2022). Enhancing the quality of cognitive behavioral therapy in community mental health through artificial intelligence generated fidelity feedback (Project AFFECT): a study protocol. BMC Health Services Research, 22(1), 1177. https://doi.org/10.1186/s12913-022-08519-9
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Tanana, M. J., Soma, C. S., Kuo, P. B., Bertagnolli, N. M., Dembe, A., Pace, B. T., Srikumar, V., Atkins, D. C., & Imel, Z. E. (2021). How do you feel? Using natural language processing to automatically rate emotion in psychotherapy. Behavior Research Methods, 53(5), 2069–2082. https://doi.org/10.3758/s13428-020-01531-z
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As its capabilities and hype grow exponentially, the role of artificial intelligence (AI) in behavioral health care raises key legal, regulatory, and ethical questions. The pace at which AI has evolved - from simple predictive models to generative AI for clinical notes to chatbots offering clinical conversations - show AI’s potential to transform the behavioral health landscape. Despite emerging frameworks and guidelines, key questions remain about privacy, equity, scope of practice, or other essential guardrails.
We bring together experts to discuss ethical development and deployment of AI in behavioral health. Panelist 1 is a Computer Scientist and Associate Professor in the Kahlert School of Computing at the University of Utah. He has published more than 75 scientific articles on machine learning and natural language processing, with funding from the National Science Foundation, National Institute of Health (NIH), Intel, Google, and Nvidia. Panelist 2 was founding Executive Director of the 988 Suicide and Crisis Lifeline now serves as President of Research Development at Behavioral Health Link, where he works exploring the integration of AI in crisis services on a national scale. Panelist 3 is a licensed clinical psychologist and implementation scientist with 20 years of NIH funding, working on generative AI to support delivery and implementation of EBPs at scale. Panelist 4 is CEO of a tech start-up that has developed extensive AI-based EBP training, supervision, and fidelity-measurement tools through NIH funding and contracts with mental health systems.
The moderator, an expert in implementation of EBPs in public mental health systems and AI-based tools to scale these efforts, leads a discussion focused on such themes as, How do we mitigate risk that AI algorithms encode bias and inequities? What constitutes appropriate use of personal data, especially for commercial purposes? Who is responsible if an AI makes or contributes to a clinical error? Is it ethical for an AI to directly deliver care? How do we think about regulation and legal implications for AI tools that develop more rapidly than regulations can be updated? After audience questions, we close with implications for the rapidly-developing intersection of AI and behavioral healthcare.

Learning Objectives:

At the end of this session, the learner will be able to:
  1. Provide a definition of artificial intelligence, including differences between general and specific AIs and the importance of the distinction.
  2. Discuss implications of the disconnect between fast paced AI technology development and slower paced regulation change in behavioral health.
  3. Describe the ways in which AI algorithms can encode bias and inequities, as well as strategies to try to reduce their impact.
  4. Discuss considerations in the use of personal client data to develop and refine AIs, especially for commercial purposes.
  5. Describe two specific risks inherent in the use of AI in behavioral health care, including ideas for mitigating those risks.