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 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.

Topics

The goal of this workshop is to bring together the causal inference, artificial intelligence, and behavior science communities, gathering insights from each of these fields to facilitate collaboration and adaptation of theoretical and domain-specific knowledge amongst them. There will be two keynote speakers (including the MacArthur award-winning behavioral economist and neuroscientist Colin Camerer), two invited speakers, a panel discussion of early career researchers working at the intersection of these fields, a submitted paper session and a poster session from participants. We invite thought-provoking submissions on a range of topics in these fields, including:

• Intervention design
• Adaptive/optimal treatments
• Heterogeneity estimation
• Optimal assignment rules
• Targeted nudges
• Bias/equity in algorithmic decision-making
• Mental health/wellness; habit formation
• Social media interventions

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 the best posters for spotlight talks (2 minutes each). We will end the workshop with a panel discussion by top researchers from these fields to enlist 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. AAAI, specifically, is a great venue for our workshop because its audience spans many ML and AI communities. We invite novel contributions following the AAAI-22 formatting guidelines, camera-ready style. 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 (including references) and short/poster papers with 2-4 pages. References will not count towards the page limit. Submission will be accepted via Easychair. 

Invited Speakers

• Colin Camerer
• Susan Murphy
• More details coming...

Organizing Committee

• Lyle Ungar
• Rahul Ladhania
• Michael Sobolev
• Linnea Gandhi

Contact

For general inquiries about AI4BC, please write to ai4behaviorchange at gmail dot com.