Association for Behavior Analysis International

The Association for Behavior Analysis International® (ABAI) is a nonprofit membership organization with the mission to contribute to the well-being of society by developing, enhancing, and supporting the growth and vitality of the science of behavior analysis through research, education, and practice.

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49th Annual Convention; Denver, CO; 2023

Event Details


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Invited Paper Session #36
CE Offered: BACB
From HAL 9000 to Wall-E: How Artificial Intelligence Can Improve Clinical Decision-Making in Autism
Saturday, May 27, 2023
12:00 PM–12:50 PM
Convention Center Four Seasons Ballroom 2/3
Area: AUT; Domain: Applied Research
Chair: Regina A. Carroll (University of Nebraska Medical Center Munroe-Meyer Institute)
CE Instructor: Marc J. Lanovaz, Ph.D.
Presenting Author: MARC J. LANOVAZ (Université de Montréal)
Abstract:

Artificial intelligence and machine learning are currently revolutionizing how we work and interact in our daily lives. In behavior analysis, one specific area of application that shows tremendous potential involves the assessment and treatment of autism. However, behavior analysts have been slow to adopt machine learning algorithms despite their promising nature. This presentation aims to provide a gentle introduction to artificial intelligence and machine learning while discussing potential applications to autism services. First, the invited session will define and describe what are artificial intelligence and machine learning. Then, the talk will cover recent exemplars of machine learning in autism research from the presenter’s work. Some exemplars include the monitoring of treatment progress, the identification of behavioral function, the selection of behavioral interventions, and the measurement of stereotypy. Finally, some benefits and drawbacks of applying machine learning to solve problems of social significance will be discussed. Overall, the presentation should provide a balanced overview of what artificial intelligence and machine learning may do (and not do) to support both practitioners and researchers in autism.

Instruction Level: Intermediate
Target Audience:

Currently practicing behavior analysts and advanced graduate students

Learning Objectives: At the conclusion of the session, participants will be able to: (1) Describe the basic methodological logic underlying supervised machine learning; (2) Explain at least one potential application of machine learning to improve services in autism; (3) Name at least one benefit and one drawback of using machine learning in autism.
 
MARC J. LANOVAZ (Université de Montréal)
Marc J. Lanovaz, Ph.D., BCBA-D, is a professor at the École de psychoéducation of the Université de Montréal and Researcher at the Institut universitaire en santé mentale de Montréal in Canada. The work in his lab has been funded by several major granting agencies such as the Canadian Institutes for Health Research, the Social Sciences and Humanities Research Council, and Québec’s Research Funds. His research program currently involves the use of artificial intelligence, machine learning, and technology to improve the delivery of behavior analytic services. Dr. Lanovaz has authored more than eighty publications on diverse topics such as clinical decision-making, parent training, early intervention, and challenging behavior in individuals with developmental disability.
 

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