Sunnie
S. Y. Kim

Full List of Papers

I primarily do AI + HCI research on improving explainability and fairness of AI systems and helping people have appropriate understanding and trust in them. But I have also done other research with collaborators in psychology, neuroscience, environmental science, and art. I really enjoy and value interdisciplinary research.

2024

"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 2024PAPEROSF

* Featured in Axios, New Scientist, ACM showcase, and the Human-Centered AI medium publication as Good Reads in Human-Centered AI.

Allowing Humans to Interactively Guide Machines Where to Look Does Not Always Improve Human-AI Team's Classification Accuracy
Giang Nguyen, Mohammad Reza Taesiri, Sunnie S. Y. Kim, Anh Totti Nguyen
CVPR 2024 XAI4CVPAPERCODEDEMO

Establishing Appropriate Trust in AI through Transparency and Explainability
Sunnie S. Y. Kim
CHI EA 2024PAPER

* Research description for the CHI 2024 Doctoral Consortium.

Human-Centered Explainable AI (HCXAI): Reloading Explainability in the Era of Large Language Models (LLMs)
Upol Ehsan, Elizabeth Anne Watkins, Philipp Wintersberger, Carina Manger, Sunnie S. Y. Kim, Niels van Berkel, Andreas Riener, Mark O. Riedl
CHI EA 2024PAPER

* Proposal for the CHI 2024 Human-Centered Explainable AI Workshop.

2023

"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 MENTIONPAPERWEBSITETALK

* 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
Sunnie S. Y. Kim, Elizabeth Anne Watkins, Olga Russakovsky, Ruth Fong, Andrés Monroy-Hernández
FAccT 2023PAPERWEBSITETALK

* 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 2023PAPERCODE

UFO: A Unified Method for Controlling Understandability and Faithfulness Objectives in Concept-based Explanations for CNNs
Vikram V. Ramaswamy, Sunnie S. Y. Kim, Ruth Fong, Olga Russakovsky
PAPER

2022

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

* 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.

ELUDE: Generating Interpretable Explanations via a Decomposition into Labelled and Unlabelled Features
Vikram V. Ramaswamy, Sunnie S. Y. Kim, Ruth Fong, Olga Russakovsky
CVPR 2022 XAI4CVPAPER

Shallow Neural Networks Trained to Detect Collisions Recover Features of Visual Loom-Selective Neurons
Baohua Zhou, Zifan Li, Sunnie S. Y. Kim, John Lafferty, Damon A. Clark
eLife 2022PAPERCODE

2021

Fair Attribute Classification through Latent Space De-biasing
Vikram V. Ramaswamy, Sunnie S. Y. Kim, Olga Russakovsky
CVPR 2021PAPERWEBSITECODEDEMOTALK

* 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).

Information-Theoretic Segmentation by Inpainting Error Maximization
Pedro Savarese, Sunnie S. Y. Kim, Michael Maire, Gregory Shakhnarovich, David McAllester
CVPR 2021PAPERWEBSITE

Cleaning and Structuring the Label Space of the iMet Collection 2020
Vivien Nguyen*, Sunnie S. Y. Kim*
CVPR 2021 FGVCPAPERCODE

[Re] Don't Judge an Object by Its Context: Learning to Overcome Contextual Bias
Sunnie S. Y. Kim, Sharon Zhang, Nicole Meister, Olga Russakovsky
ReScience 2021PAPERCODE

* Selected for publication from the ML Reproducibility Challenge 2020.

2020

Deformable Style Transfer
Sunnie S. Y. Kim, Nicholas Kolkin, Jason Salavon, Gregory Shakhnarovich
ECCV 2020PAPERWEBSITECODEDEMOTALK

* Received 260+ stars on GitHub. Also presented at the ECCV 2020 Women in Computer Vision Workshop.

2018

Environmental Performance Index
Zachary A. Wendling, Daniel C. Esty, John W. Emerson, Marc A. Levy, Alex de Sherbinin, ..., Sunnie S. Y. Kim et al.
WORLD ECONOMIC FORUM 2018PAPERWEBSITEARTICLEDISCUSSION

* Presented at the World Economic Forum. Covered by international media outlets. As the data team lead, I built the full data pipeline and led the analysis work.