Colloquium: The Value of Information in Model-Assisted Decisions

Join us Tuesday, November 11 for our next invited speaker of the semester! 

Dr. Jessica Hullman will be presenting at 11 AM in the Z. Smith Reynolds (ZSR) Auditorium, Room 404 (details below).

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 to transform how 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 to achieve human-AI complementarity, quantifying and expressing prediction uncertainty, and leveraging LLMs in behavioral science. Her work has led to multiple best paper and honorable mention awards at conferences, a Microsoft Faculty award, and NSF CAREER, among other honors.

Dr. Hullmans’s talk is entitled “The Value of Information in Model-Assisted Decisions”

Abstract:  Judgments from humans and one or more artificial agents are increasingly combined for decisions, with the expectation that the decisions of the team will outperform those of individual agents. However, simple approaches to providing human decision makers with AI support and evaluating team performance can lead to apparent failures in team performance even when the agents are thought to possess complementary information. I’ll discuss measurement frameworks we’ve developed that apply statistical decision theory and information economics to address questions at the human-agent interface, including 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) explanations in principled ways.