Sunnie
S. Y. Kim
I am a PhD candidate in Computer Science at Princeton University advised by Olga Russakovsky. I also frequently work with Andrés Monroy-Hernández, Ruth Fong, and Tania Lombrozo at Princeton, as well as Jenn Wortman Vaughan and Q. Vera Liao at Microsoft Research FATE. My PhD is supported by the NSF Graduate Research Fellowship, and I was recently recognized as a Siebel Scholar and a Rising Star in EECS 🌟
I work on responsible and human-centered AI — specifically, on improving the explainability and fairness of AI systems and helping people have appropriate understanding and trust in them. I publish in both AI and HCI venues and enjoy organizing events that connect the two communities.
Previously, I received a BSc degree in Statistics and Data Science at Yale University where I worked with John Lafferty, Jay Emerson, and Woo-kyoung Ahn. I have also spent time at TTI-Chicago working with Greg Shakhnarovich.
My first name is pronounced as sunny 🔆 and I use she/her/hers pronouns. In my free time, I like to run, play tennis, and read Korean books.
I am on the academic and industry job market. Please reach out if you think I would be a good fit!
News
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Selected Papers
See the full list of papers here
"I'm Not Sure, But...": Examining the Impact of Large Language Models' Uncertainty Expression on User Reliance and Trust
FAccT 2024 ●
PAPER ●
OSF
* Featured in Axios, New Scientist, ACM showcase, Microsoft's New Future of Work Report, and the Human-Centered AI Medium publication as Good Reads in Human-Centered AI.
"Help Me Help the AI": Understanding How Explainability Can Support Human-AI Interaction
CHI 2023 HONORABLE MENTION ●
PAPER ●
WEBSITE ●
TALK
* Featured in the Human-Centered AI Medium publication as CHI 2023 Editors' Choice. Also presented at the NeurIPS 2022 Human-Centered AI Workshop (spotlight), the CHI 2023 Human-Centered Explainable AI Workshop, and the ECCV 2024 Explainable Computer Vision Workshop (invited talk).
Humans, AI, and Context: Understanding End-Users’ Trust in a Real-World Computer Vision Application
FAccT 2023 ●
PAPER ●
WEBSITE ●
TALK
* Featured in the Montreal AI Ethics Institute's blog. Also presented at the CHI 2023 Trust and Reliance in AI-assisted Tasks Workshop.
Overlooked Factors in Concept-based Explanations: Dataset Choice, Concept Learnability, and Human Capability
CVPR 2023 ●
PAPER ●
CODE ●
TALK
HIVE: Evaluating the Human Interpretability of Visual Explanations
ECCV 2022 ●
PAPER ●
WEBSITE ●
CODE ●
TALK
* Also presented at the CVPR 2022 Explainable AI for Computer Vision Workshop (spotlight), the CHI 2022 Human-Centered Explainable AI Workshop (spotlight), and the CVPR 2022 Women in Computer Vision Workshop.
Fair Attribute Classification through Latent Space De-biasing
CVPR 2021 ●
PAPER ●
WEBSITE ●
CODE ●
DEMO ●
TALK
* Featured in Coursera's GANs Specialization course and the MIT Press book Foundations of Computer Vision. Also presented at the CVPR 2021 Responsible Computer Vision Workshop (invited talk) and the CVPR 2021 Women in Computer Vision Workshop (invited talk).
Organized Workshops
The 3rd Explainable AI for Computer Vision (XAI4CV) Workshop
CVPR 2024 ●
WEBSITE
The 4th Human-Centered Explainable AI (HCXAI) Workshop
CHI 2024 ●
WEBSITE ●
PROPOSAL
The 2nd Explainable AI for Computer Vision (XAI4CV) Workshop
CVPR 2023 ●
WEBSITE
The 11th Women in Computer Vision (WiCV) Workshop
CVPR 2023 ●
WEBSITE ●
REPORT
Other Important Things
Teaching & Outreach
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Mentoring & Community Building
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Reviewing
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Volunteering
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Committee
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