Advancing Responsible AI with Human-Centered Evaluation

Sunnie S. Y. Kim is a Ph.D. candidate in Computer Science at Princeton University advised by Olga Russakovsky. She works on responsible AI and human-AI interaction — specifically, on improving the explainability and fairness of AI systems and helping people have appropriate understanding and trust in them. Her research has been published in both AI and HCI venues (e.g., CVPR, ECCV, CHI, FAccT), and she has organized multiple workshops connecting the two communities. She has been recognized by the NSF GRFP, Siebel Scholars, and Rising Stars in EECS, and has interned at Microsoft Research with the FATE group. Prior to graduate school, she received a BSc degree in Statistics and Data Science from Yale University and spent a year at Toyota Technological Institute at Chicago.

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Talk: Advancing Responsible AI with Human-Centered Evaluation

Abstract: As AI technologies are increasingly transforming how we live, work, and communicate, AI evaluation must take a human-centered approach to realistically reflect real-world performance and impact. In this talk, I will discuss how to advance human-centered evaluation, and subsequently, responsible development of AI, by integrating knowledge and methods from AI and HCI. First, using explainable AI as an example, I will highlight the challenges and necessity of human (as opposed to automatic) evaluation. Second, I will illustrate the importance of contextualized evaluation with real users, revisiting key assumptions in explainable AI research. Finally, I will present empirical insights into human-AI interaction, demonstrating how users perceive and act upon common AI behaviors (e.g., LLMs providing explanations and sources). I will conclude by discussing the implications of these findings and future directions for responsible AI development.