Theories of decision making are implemented in models that predict and explain behavior interms of latent cognitive processes. But where do these models come from, and how are theyinstantiated in the brain? In this paper, we examine several avenues where artificial intelligence(AI) and machine learning (ML) can benefit decision theory by providing new methodsfor developing and testing cognitive models. First, machine learning can be used to efficientlyestimate the values of latent parameters in cognitive models, and assign posterior probabilitiesto competing models of the same observed data. Second, models of decision behavior canbe embedded within artificially intelligent systems to allow them to make inferences abouthuman counterparts (goals, abilities, cognition) in real time, equipping AI with tools to interactsocially. Third, AI can be used to understand how evolutionary and learning processes giverise to the cognitive abilities that support decision-making. Finally, the tools of experimentalpsychology and decision sciences can be applied to better understand the “black boxes” ofneural networks by systematically testing input-output (stimulus-response) relationships. Puttogether, we suggest that merging ML/AI into decision modeling – and vice versa – is a promisingpath toward many long-term benefits for both fields.