The value of information in model assisted decision-making

Abstract: The widespread adoption of AI and machine learning models in 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.

Bio: Jessica Hullman is Ginni Rometty Professor of Computer Science and a Faculty Fellow at the Institute for Policy Research at Northwestern University. Her research develops theoretical frameworks and interface tools for helping people combine their knowledge with statistical models. Her work draws on foundation models of decision-making under uncertainty such as Bayesian decision theory while addressing real world applied problems at the interface between humans and statistical models. Hullman’s current research pursues methods for designing explanations and quantifying uncertainty for AI-assisted decision-making, as well as evaluating AI-human team performance. Her work has led to multiple best paper and honorable mention awards at top visualization and HCI venues, a Microsoft Faculty award, and NSF CAREER, among other honors.