Across many application areas, machine learning (ML) systems rely on human data. Yet these systems often leave unmodelled the psychological processes that generate human data, or abstract these rich mental processes into simple models. Fortunately, there's a field full of insights about human behavior: the behavioral sciences. However, these insights are often qualitative. Integrating them into machine learning systems requires converting them into computable models and designing machine learning systems to incorporate them. The goal of this workshop is to explore incorporating insights from the behavioral sciences into machine learning systems. Our workshop will focus on one specific question in this broad area: how can we incorporate behavioral insights into formal computable models? Translating behavioral insights into computable models would enable them to interact with ML systems: behavioral models can improve ML models, and vice-versa. We hope to bring together computer scientists across many subfields with behavioral scientists to drive progress in this interdisciplinary area.