Sunnie
S. Y. Kim

I'm a research scientist at Apple in the Human-Centered Machine Intelligence & Responsible AI group.

I work on responsible AI and human-AI interaction to build AI technologies that users can safely and successfully interact with. I especially like to do careful, human-centered evaluations grounded in real user needs and contexts.

My research has been published in top AI, HCI, and FATE venues, recognized with paper awards, and featured in various media outlets. I have also received several honors including the NSF Graduate Research Fellowship, the Rising Stars in EECS Recognition, the Siebel Scholars Award, and the Korea Presidential Science Scholarship.

I received a PhD in Computer Science from Princeton University where I was fortunate to work with Olga Russakovsky and Andrés Monroy-Hernández, and collaborate with Jenn Wortman Vaughan and Q. Vera Liao at Microsoft Research FATE. Prior to graduate school, I received a BSc in Statistics and Data Science at Yale University where I worked with John Lafferty and Jay Emerson.

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.

  CV

News

07/2025: Moved to Seattle and started a new position at Apple.
06/2025: Attended FAccT 2025 in Athens where I served as a proceedings co-chair.
05/2025: Completed my PhD at Princeton University 🎉 A copy of my dissertation is available here.
This Past Year: Had a lovely time visiting and giving talks (some virtually) at KAIST, MILA, Cornell Tech, SNU, Johns Hopkins, BU, Cornell, Apple, Yonsei University, and NAVER.

Selected papers

See the full list of papers here

Fostering Appropriate Reliance on Large Language Models: The Role of Explanations, Sources, and Inconsistencies
Sunnie S. Y. Kim, Jennifer Wortman Vaughan, Q. Vera Liao, Tania Lombrozo, Olga Russakovsky
CHI 2025 HONORABLE MENTION ● PAPER ● TALK

* Featured in Microsoft's New Future of Work Report and presented at 10+ places through invited and contributed talks.

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

* One of the top 10 cited CHI papers in 2023-2024 (as of Dec 2024). Featured in the Human-Centered AI Medium publication as CHI 2023 Editors' Choice. Also presented at the NeurIPS 2022 Human-Centered AI Workshop (spotlight), CHI 2023 Human-Centered Explainable AI Workshop (spotlight), ECCV 2024 Explainable Computer Vision Workshop (invited talk), and NYC Computer Vision Day 2024 (lightning talk).

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 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
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

* Also presented at the CVPR 2022 Explainable AI for Computer Vision Workshop (spotlight), CHI 2022 Human-Centered Explainable AI Workshop (spotlight), and 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. Also presented at the CVPR 2021 Responsible Computer Vision Workshop (invited talk) and CVPR 2021 Women in Computer Vision Workshop (invited talk).

Other important things

Organizing Committee
●  FAccT 2025, Proceedings Co-Chair
●  CVPR 2025 Workshop on Explainable AI for Computer Vision (XAI4CV), Co-Organizer
●  NYC Computer Vision Day 2025, Event Program Committee
●  CVPR 2024 Workshop on Explainable AI for Computer Vision (XAI4CV), Co-Organizer
●  CHI 2024 Workshop on Human-Centered Explainable AI (HCXAI), Co-Organizer
●  CVPR 2023 Workshop for Women in Computer Vision (WiCV), Co-Organizer
●  CVPR 2023 Workshop on Explainable AI for Computer Vision (XAI4CV), Co-Organizer
●  NESS NextGen Data Science Day 2018, Local Organizing Committee

Program Committee & Reviewing
●  NeurIPS (2025 Main track & Ethics review)
●  CVPR (2022, 2023, 2024, 2025), ICCV (2021, 2023), ECCV (2022, 2024)
●  CHI (2023, 2024, 2025), FAccT (2023, 2024, 2025), AIES (2024), SaTML (2023)
●  Various workshops at NeurIPS, CHI, CVPR, ICML, AAAI (2021—Now)
●  ML Reproducibility Challenge (2020, 2021, 2022)

Mentoring
●  Allison Chen, CS PhD student at Princeton, 2024—2025
●  Indu Panigrahi, CS Master's student at Princeton, Incoming CS PhD student at UIUC, 2024—2025
●  Rohan Jinturkar, CS Undergrad at Princeton, 2022—2023
●  Nicole Meister, CS Undergrad at Princeton, Now EE PhD student at Stanford, 2020—2022
●  Sharon Zhang, CS Undergrad at Princeton, Now CS PhD student at Stanford, 2020—2021
●  Princeton Computer Science G1 Mentoring Program, 2022—2023
●  Princeton Computer Science Graduate Applicant Support Program, 2021—2022

Teaching
●  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 & More
●  FAccT, CVPR, ECCV, NeurIPS, ICLR, NSF SafeTAI Workshop, Student Volunteer, 2019—2024
●  Explainable AI Slack and Twitter Community, Co-Organizer, 2022—2023
●  Princeton Computer Science Graduate Admissions Committee, Application Reviewer, 2021
●  COVID Translate Project, Volunteer Translator, 2020
●  Yale Dimensions Organization for Women and Other Minorities in Math, Co-Founder, 2017—2019
●  Yale S&DS Departmental Student Advisory Committee, Co-Founding Member, 2017—2019

Please reach out!

●  I was on the academic and industry job market (2024—2025 cycle). During this time, I received support from countless people, including people I haven't met before. Happy to be a resource and share anything that would be helpful.
●  I benefited from various mentoring programs in grad school (e.g., doctoral consortiums and the Rising Stars in EECS workshop). I would be excited to contribute back as a mentor or in other ways.
●  I enjoy co-organizing and participating in workshops, tutorials, panels, etc., especially those focused on connecting different communities.
●  Please reach out if any of these apply! :)

Contact

sunniesuhyoung [at] gmail.com
google scholar
linkedin
github
bluesky
mastodon
twitter/X

Website template credit to Abhraneel