Helpful AI Models - Building Human-Computer Teams for Learning, Negotiation, and Fact-Checking

Attend this plenary conference talk exploring how AI models should be optimized for human-computer collaboration rather than just accuracy or user satisfaction. Learn about three research examples demonstrating effective human-AI teamwork: vocabulary learning through adaptive flashcard scheduling that combines perceived and actual helpfulness, strategic negotiation assistance in the board game Diplomacy using grounded statement analysis and value functions, and collaborative false claim identification where computers help humans detect misinformation when the AI avoids confident incorrect responses. Discover how these applications lead to broader questions about measuring and comparing human versus computer skills, with insights into designing AI systems that truly enhance human capabilities rather than simply replacing them. The presentation, delivered by Jordan Boyd-Graber from the University of Maryland, draws from extensive research in human-centered AI evaluation and interactive machine learning systems.