The AAAI-22 Workshop on AI For Behavior Change held at the Thirty-Sixth AAAI Conference on Artificial Intelligence (AAAI-22)

February 28, 2022


In decision-making domains as wide-ranging as medication adherence, vaccination, college enrollment, retirement savings, and energy consumption, behavioral interventions have been shown to encourage people towards making better choices. It is important to learn how to use AI effectively in these areas in order to be able to motivate and help people to take actions that maximize their welfare.

At least three research trends are informing insights in this field. First, large data sources, both conventionally used in social sciences (EHRs, health claims, credit card use, college attendance records) and unconventional (social networks, fitness apps), are now available, and are increasingly used to personalize interventions. These datasets can be leveraged to learn individuals’ behavioral patterns, identify individuals at risk of making sub-optimal or harmful choices, and target them with behavioral interventions to prevent harm or improve well-being. Second, psychological experiments in laboratories and in the field, in partnership with technology companies (e.g., using apps), to measure behavioral outcomes are being increasingly used for informing intervention design. Finally, there is an increasing interest in AI in moving beyond traditional supervised learning approaches towards learning causal models, which can support the identification of targeted behavioral interventions. These research trends inform the need to explore the intersection of AI with behavioral science and causal inference, and how they can come together for applications in the social and health sciences.

This proposed workshop will build upon successes and learnings from last year’s successful AI for Behavior Change workshop, and will focus on on advances in AI and ML that aim to (1) design and target optimal interventions; (2) explore bias and equity in the context of decision-making and (3) exploit datasets in domains spanning mobile health, social media use, electronic health records, college attendance records, fitness apps, etc. for causal estimation in behavioral science.


The goal of this workshop is to bring together scholars from the causal inference, artificial intelligence, and behavior science (eg. psychology, behavioral economics) communities, gathering insights from each of these fields to facilitate collaboration and adaptation of theoretical and domain-specific knowledge amongst them. We invite thought-provoking submissions on a range of topics across multiple disciplines, including, but not limited to the following areas:

  • Intervention design
  • Adaptive/optimal treatment assignment
  • Heterogeneity estimation
  • Targeted nudges
  • Bias/equity in algorithmic decision-making
  • Vaccine hesitancy/Vaccine uptake
  • Mental health/wellness
  • Habit formation
  • Social media interventions
  • Psychological science
  • Precision health


The full-day workshop will start with a keynote talk, followed by an invited talk and contributed paper presentations in the morning. The post-lunch session will feature a second keynote talk, two invited talks, and contributed paper presentations. Papers more suited for a poster, rather than a presentation, would be invited for a poster session. We will also select the best posters for spotlight talks (2 minutes each). We will end the workshop with a panel discussion by top researchers in the field. We are also actively exploring options to conduct the workshop in a hybrid format, with participants able to join in remotely and in-person.

Submission Guidelines

The audience of this workshop will be researchers and students from a wide array of disciplines including, but not limited to, statistics, computer science, economics, public policy, psychology, management, and decision science, who work at the intersection of causal inference, machine learning, and behavior science. We invite novel contributions following the AAAI-22 formatting guidelines, camera-ready style. Work that is in submission or under review is also acceptable. Submissions will be peer reviewed, single-blinded. Submissions will be assessed based on their novelty, technical quality, significance of impact, interest, clarity, relevance, and reproducibility. We accept two types of submissions - full research papers no longer than 8 pages and short/poster papers with 2-4 pages. References will not count towards the page limit. Submissions will be accepted via the Easychair submission website.

Invited Speakers

Colin Camerer (keynote)
Behavioral Economics and Neuroscience
California Institute of Technology

Susan Murphy (keynote)
Statistics and Computer Science
Harvard University

Rayid Ghani
Data Science and Public Policy
Carnegie Mellon University

Jenna Wiens
Machine Learning and Healthcare
University of Michigan


Dean Knox
Compuational Social Science
University of Pennsylvania

Jennifer Logg
Georgetown University

Phebe Vayanos
Industrial & Systems Engineering & Computer Science
University of Southern California

Partners and Sponsors


Lyle Ungar
University of Pennsylvania

Rahul Ladhania (primary contact)
University of Michigan

Linnea Gandhi
University of Pennsylvania

Michael Sobolev
Cornell Tech

Key Dates

  • Submission Deadline: November 28, 2021 11.59 pm Anywhere on Earth (via Easychair)
  • Notification of acceptance/rejection: December 26, 2021
  • Registration for Accepted Authors: December 31, 2021
  • Early Bird Registration for everyone else: December 31, 2021
  • Workshop Date: February 28, 2022


For any questions, please reach out to us at ai4behaviorchange at gmail dot com