The AAAI-21 Workshop on AI For Behavior Change held at the Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI-21)

February 8, 2021
A Virtual Conference
Zoom Recordings

Introduction

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 workshop will focus on AI and ML or psychological theory based approaches that can (1) identify individuals in need of behavioral interventions, and/or predict when they need them; (2) help design and target optimal interventions; and (3) exploit observational and/or experimental datasets in domains including social media, health records, claims data, fitness apps, etc. for causal estimation in the behavior science world.

Topics

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 treatment assignment
  • Heterogeneity estimation
  • Optimal assignment rules
  • Targeted nudges
  • Observational-experimental data
  • Mental health/wellness
  • Habit formation
  • Social media interventions
  • Psychological science
  • Precision health

Format

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 up to 5 best posters for spotlight talks (2 minutes each). We will also organize breakout “lunch rooms” hosted by our partners with specific themes, aimed at faciliating conversations and relationship building among scholars and to discuss future directions and enhancement to this workshop.

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-21 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

Susan Athey (keynote)
Economics of Technology
Stanford University

Sendhil Mullainathan (keynote)
Computation and Behavioral Science
University of Chicago

Eric Tchetgen Tchetgen
Statistics
University of Pennsylvania

Jon Kleinberg
Computer Science
Cornell University

Munmun De Choudhury
Interactive Computing
Georgia Tech

Partners and Sponsors

Behavior Change For Good
University of Pennsylvania

Center for Applied Artificial Intelligence
Chicago Booth

Psychology Of Technology Institute

Organization

Organizing Committee

Lyle Ungar
University of Pennsylvania

Sendhil Mullainathan
University of Chicago

Eric Tchetgen Tchetgen
University of Pennsylvania

Rahul Ladhania (primary contact)
University of Michigan

Tony Liu
University of Pennsylvania

Program Committee (tentative)

Key Dates

  • (Updated) Submission Deadline: November 13, 2020 11.59 pm Anywhere on Earth (via Easychair)
  • Notification of acceptance/rejection: December 8, 2020
  • Registration for Accepted Authors: December 18, 2020 (Link here)
  • Early Bird Registration for everyone else: January 15, 2021 (Link here)
  • Workshop Date: February 8, 2021

Contact

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