Designing Responsible AI: Lessons from Physics for Health, Equity, and Society

Artificial intelligence systems are now influencing scientific discovery, public services, and societal decision-making at unprecedented scales. As these models grow more capable, designing them responsibly requires not only new techniques, but new perspectives. Physicists are uniquely positioned to shape this landscape. Our community brings deep expertise in experiment design, uncertainty quantification, model validation under distribution shift, and interpreting complex systems through symmetry, structure, and inductive bias. In this talk, I will outline how these principles translate to open challenges in responsible AI, drawing from examples in science-aligned foundation models, mechanistic interpretability, and benchmarking AI "scientist-like'' reasoning. I highlight opportunities for physicists to contribute rigorous measurement frameworks, to interrogate emergent behaviors in large models, and to build evaluation protocols grounded in hypothesis-driven inquiry rather than surface-level performance. Finally, I discuss the critical role of the physics community in shaping governance norms, standards, and public communication around AI safety and equity—emphasizing that responsible AI is not solely a technical question, but a scientific responsibility.