Who Gets Represented in Robot Intelligence? Human-Robot Interaction, Societal Context, and the Future of Autonomy (Keynote)

Abstract

Human-robot interaction has long asked how robots should perceive, interpret, and respond to people. But as autonomous systems move into increasingly varied human environments, a deeper question becomes unavoidable: who gets represented in robot intelligence at all? Which languages, social contexts, assumptions, and everyday realities are reflected in the systems we build, and what happens when they are not? In this talk, I argue that representation is not only a social concern. It is also central to building intelligent systems that work robustly in the real world. When our models of people and context are too narrow, our systems are not merely less inclusive, but they are also less capable. Drawing on experiences from building AI communities, institutions, and initiatives in Africa, I explore how broadening participation in AI changes not only who contributes to the field, but also which problems are studied and which solutions become possible. I connect these ideas to technical questions in autonomous decision making, including how robots model others under uncertainty and how we design systems that can adapt across tasks and settings without oversimplifying the world they inhabit. The future of autonomy depends not only on making robots smarter, but on making them better able to represent the societies they are meant to serve.