The value of information in model-assisted decision-making

The widespread adoption of AI and machine learning models in society has brought increased attention to how model predictions impact decision processes in a variety of domains. I will describe tools that apply statistical decision theory and information economics to address pressing question at the human/AI interface. These include: how to evaluate when a decision-maker appropriately relies on model predictions, when a human or AI agent could better exploit available contextual information, and how to evaluate (and design) prediction explanations. I will also discuss some cases where statistical theory falls short of providing insight into how people may use predictions for decisions.