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Join us for the Johns Hopkins Data Science and AI Institute Spring 2025 Symposium

Human Alignment of AI
Tuesday, April 22, 2025
Symposium | 10 a.m. to 4 p.m. | Shriver Hall
Poster Session | 4 p.m.
| Great Hall


This symposium will bring together scientists, academics, policymakers, and ethicists to discuss how new AI tools align with humans in terms of their inner workings, performance, values, and goals.

Speakers Include:

Tom Griffiths

Tom Griffiths is the Henry R. Luce Professor of Information Technology, Consciousness and Culture in the Departments of Psychology and Computer Science at Princeton University. His research explores connections between human and machine learning, using ideas from statistics and artificial intelligence to understand how people solve the challenging computational problems they encounter in everyday life. He is also co-author of the book, Algorithms to Live By, introducing ideas from computer science and cognitive science to a general audience.

Ivor Braden Horn

Dr. Ivor Braden Horn has over two decades of experience as an advisor, investor, physician, and technology executive with experience in entrepreneurial, venture, academic medicine, health systems, and research organizations. She is internationally recognized for her work in health equity and healthcare innovation. As the founding director and first Chief Health Equity Officer at Google, she led strategies to integrate health equity into research and product development. Dr. Horn has held significant roles at Accolade, Seattle Children’s Hospital, and the University of Washington School of Medicine. She currently serves on the boards of Boston Children’s Hospital, Care Academy and Acclinate.

Thomas Icard

Thomas Icard is the Clarence Irving Lewis Professor of Philosophy and Professor of Computer Science, by courtesy, at Stanford University. He works at the intersection of philosophy, cognitive science, and computer science, especially on topics related to causality, language, decision making, and reasoning. A driving theme for much of this work is the productive tension between normative and descriptive perspectives on natural and artificial intelligence.