Title: 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, Sunnie S. Y. Kim will discuss how to advance human-centered evaluation—and subsequently, responsible development of AI—by integrating knowledge and methods from AI and human-computer interaction. First, using explainable AI as an example, she will highlight the challenges and necessity of human (as opposed to automatic) evaluation. Second, she will illustrate the importance of contextualized evaluation with real users, revisiting key assumptions in explainable AI research. Finally, Kim will present empirical insights into human-AI interaction, demonstrating how users perceive and act upon common AI behaviors (e.g., large language models providing explanations and sources). She will conclude by discussing the implications of these findings and future directions for responsible AI development.
Bio: Sunnie S. Y. Kim is a PhD 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 of and trust in them. Her research has been published in both AI and human-computer interaction venues (e.g., the Conference on Computer Vision and Pattern Recognition; the European Conference on Computer Vision; the ACM Conference on Human Factors in Computing Systems; the ACM Conference on Fairness, Accountability, and Transparency), and she has organized multiple workshops connecting the two communities. She has been recognized by the NSF Graduate Research Fellowship Program, the Siebel Scholars program, and Rising Stars in Electrical Engineering and Computer Science, and has interned at Microsoft Research with the Fairness, Accountability, Transparency, and Ethics in AI group. Prior to graduate school, she received a BSc in statistics and data science from Yale University and spent a year at Toyota Technological Institute at Chicago.