Sunnie
S. Y. Kim

I am a PhD student in Computer Science at Princeton University advised by Olga Russakovsky. I also work closely 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 who I previously interned with. My PhD is supported by the NSF Graduate Research Fellowship.

I work on responsible AI—specifically, on improving transparency, 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!

News

06/2024: Will be attending FAccT 2024 in Rio de Janeiro to present "I'm Not Sure, But..."
05/2024: Gave a talk at the IBS Data Science Group on Establishing Appropriate Trust in AI through Transparency and Explainability.
05/2024: Attended CHI 2024 in Honolulu, participated in the Doctoral Consortium, and organized the Human-Centered Explainable AI Workshop.
04/2024: Gave a lightning talk at the NYC Computer Vision Day on Bridging Computer Vision and HCI.
02/2024: Gave a talk at the KAIST Graduate School of AI on Supporting End-Users' Interaction with AI through Transparency and Explainability.

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
Sunnie S. Y. Kim, Q. Vera Liao, Mihaela Vorvoreanu, Stephanie Ballard, Jennifer Wortman Vaughan
FAccT 2024 ● PAPER ● OSF

* Featured in 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
Sunnie S. Y. Kim, Elizabeth Anne Watkins, Olga Russakovsky, Ruth Fong, Andrés Monroy-Hernández
CHI 2023 BEST PAPER HONORABLE MENTION ● PAPER ● WEBSITE ● TALK

* Featured in the Human-Centered AI medium publication as CHI 2023 Editors' Choice. Selected as a panel presentation at the NeurIPS 2022 Human-Centered AI Workshop. Also presented at the CHI 2023 Human-Centered Explainable AI Workshop.

Humans, AI, and Context: Understanding End-Users’ Trust in a Real-World Computer Vision Application
Sunnie S. Y. Kim, Elizabeth Anne Watkins, Olga Russakovsky, Ruth Fong, Andrés Monroy-Hernández
FAccT 2023 ● PAPER ● WEBSITE ● TALK

* Featured in the Montreal AI Ethics Institute's newsletter and website. 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
Vikram V. Ramaswamy, Sunnie S. Y. Kim, Ruth Fong, Olga Russakovsky
CVPR 2023 ● PAPER ● CODE ● TALK

HIVE: Evaluating the Human Interpretability of Visual Explanations
Sunnie S. Y. Kim, Nicole Meister, Vikram V. Ramaswamy, Ruth Fong, Olga Russakovsky
ECCV 2022 ● PAPER ● WEBSITE ● CODE ● TALK

* Selected as a spotlight talk at the CVPR 2022 Explainable AI for Computer Vision Workshop. Also presented at the CHI 2022 Human-Centered Explainable AI Workshop and the CVPR 2022 Women in Computer Vision Workshop.

Fair Attribute Classification through Latent Space De-biasing
Vikram V. Ramaswamy, Sunnie S. Y. Kim, Olga Russakovsky
CVPR 2021 ● PAPER ● WEBSITE ● CODE ● DEMO ● TALK

* Featured in Coursera's GANs Specialization course and the MIT Press book Foundations of Computer Vision. Invited for talks at the CVPR 2021 Responsible Computer Vision Workshop and the CVPR 2021 Women in Computer Vision Workshop.

Organized Workshops

The 3rd Explainable AI for Computer Vision (XAI4CV) Workshop
Indu Panigrahi, Sunnie S. Y. Kim, Vikram V. Ramaswamy, Sukrut Rao, Stefan Kolek, Lenka Tětková, Jawad Tayyub, Katelyn Morrison, Pushkar Shukla, Deepti Ghadiyaram.
CVPR 2024 ● WEBSITE

The 4th Human-Centered Explainable AI (HCXAI) Workshop
Upol Ehsan, Elizabeth Anne Watkins, Philipp Wintersberger, Carina Manger, Sunnie S. Y. Kim, Niels van Berkel, Andreas Riener, Mark O. Riedl
CHI 2024 ● WEBSITE ● PROPOSAL

The 2nd Explainable AI for Computer Vision (XAI4CV) Workshop
Sunnie S. Y. Kim, Vikram V. Ramaswamy, Ruth Fong, Filip Radenovic, Abhimanyu Dubey, Deepti Ghadiyaram.
CVPR 2023 ● WEBSITE

The 11th Women in Computer Vision (WiCV) Workshop
Doris Antensteiner, Marah Halawa, Asra Aslam, Ivaxi Sheth, Sachini Herath, Ziqi Huang, Sunnie S. Y. Kim, Aparna Akula, Xin Wang.
CVPR 2023 ● WEBSITE ● REPORT

Other Important Things

Teaching & Outreach
● Princeton Computer Science Graduate Applicant Support Program, Mentor, 2021-2022
● Princeton Computer Science 429 Computer Vision, Graduate TA, 2021
● Princeton AI4ALL, Instructor, 2021
● TTI-Chicago Girls Who Code, Instructor & Co-founder, 2019-2020
● Yale Statistics & Data Science 365/565 Data Mining and Machine Learning, Undergraduate TA, 2018
● Yale Statistics & Data Science 230/530 Data Exploration and Analysis, Undergraduate TA, 2017

Community Building
● Explainable AI Slack and Twitter Community, Co-organizer, 2022-2023
● Princeton Computer Science G1 Mentoring Program, Mentor, 2022-2023
● Yale Dimensions Organization for Women and Other Minorities in Math, Co-founder, 2017-2019

Reviewing
● CVPR, ICCV, ECCV, CHI, FAccT, SaTML
● Numerous workshops at CVPR, ICML, AAAI
● ML Reproducibility Challenge (Outstanding Reviewer x 2)

Volunteering
● FAccT, CVPR, NeurIPS, ICLR, NSF Safety and Trust in AI-Enabled Systems Workshop
● COVID Translate Project

Committee
● Princeton Computer Science Graduate Admissions Committee, 2021
● Yale Statistics & Data Science Departmental Student Advisory Committee, 2017-2019

Contact

sunniesuhyoung [at] princeton.edu
google scholar
github
twitter
mastodon

Website template credit to Abhraneel