Introduction

In decision-making domains as wide-ranging as medication adherence, vaccination uptake, college enrollment, financial savings, and energy consumption, behavioral interventions have been shown to encourage people towards making better choices. AI can play an important, and in some cases crucial, role in these areas to motivate and help people take actions that maximize welfare. It is also important to be cognizant of any unintended consequences of leveraging AI in these fields, such as problems of bias that algorithmic approaches can introduce, replicate, and/or exacerbate in complex social systems.

A number of research trends are informing insights in this field. First, large data sources, both those conventionally used in social sciences (EHRs, health claims, credit card use, college attendance records) and the relatively unconventional (social networks, wearables, mobile devices), 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, to measure behavioral outcomes are increasingly used for informing intervention design. Third, 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 and flexible estimation of their effects. At the intersection of these trends is also the question of fairness - how to design or evaluate interventions fairly. 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 build upon the success of the last two editions of the AI for Behavior Change workshop, and will focus on advances in AI and ML that aim to (1) study equitable exploration for unbiased behavioral interventions, (2) design and target optimal interventions, 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 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
• Optimal assignment rules
• Targeted nudges
• Bias/equity in algorithmic decision-making
• Mental health/wellness; habit formation
• Recommender systems and digital data
• Reinforcement Learning for efficient exploration

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. Papers more suited for a poster, rather than a presentation, would be invited for a poster session. We will end the workshop with a panel discussion by researchers in the field.

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-23 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 upto 3 pages. References will not count towards the page limit. Submissions will be accepted via Easychair.

Organization

• Rahul Ladhania
• Sabina Tomkins
• Michael Sobolev
• Lyle Ungar

Contact

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