Sunnie S. Y. Kim

According to our database, Sunnie S. Y. Kim authored at least 17 papers between 2020 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

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 Nguyen

CoRR, 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

Proceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency, 2024

Establishing Appropriate Trust in AI through Transparency and Explainability.

Sunnie S. Y. Kim

Proceedings of the Extended Abstracts of the CHI Conference on Human Factors in Computing Systems, 2024

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

Proceedings of the Extended Abstracts of the CHI Conference on Human Factors in Computing Systems, 2024

2023

WiCV@CVPR2023: The Eleventh Women In Computer Vision Workshop at the Annual CVPR Conference.

Doris Antensteiner, Marah Halawa, Asra Aslam, Ivaxi Sheth, Sachini Herath, Ziqi Huang, Sunnie S. Y. Kim, Aparna Akula, Xin Wang

CoRR, 2023

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

CoRR, 2023

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

Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency, 2023

Overlooked Factors in Concept-Based Explanations: Dataset Choice, Concept Learnability, and Human Capability.

Vikram V. Ramaswamy, Sunnie S. Y. Kim, Ruth Fong, Olga Russakovsky

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 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

Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems, 2023

2022

Overlooked factors in concept-based explanations: Dataset choice, concept salience, and human capability.

Vikram V. Ramaswamy, Sunnie S. Y. Kim, Ruth Fong, Olga Russakovsky

CoRR, 2022

ELUDE: Generating interpretable explanations via a decomposition into labelled and unlabelled features.

Vikram V. Ramaswamy, Sunnie S. Y. Kim, Nicole Meister, Ruth Fong, Olga Russakovsky

CoRR, 2022

HIVE: Evaluating the Human Interpretability of Visual Explanations.

Sunnie S. Y. Kim, Nicole Meister, Vikram V. Ramaswamy, Ruth Fong, Olga Russakovsky

Proceedings of the Computer Vision - ECCV 2022, 2022

2021

Cleaning and Structuring the Label Space of the iMet Collection 2020.

Vivien Nguyen, Sunnie S. Y. Kim

CoRR, 2021

[Re] Don't Judge an Object by Its Context: Learning to Overcome Contextual Bias.

Sunnie S. Y. Kim, Sharon Zhang, Nicole Meister, Olga Russakovsky

CoRR, 2021

Information-Theoretic Segmentation by Inpainting Error Maximization.

Pedro Savarese, Sunnie S. Y. Kim, Michael Maire, Greg Shakhnarovich, David McAllester

Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2021

Fair Attribute Classification Through Latent Space De-Biasing.

Vikram V. Ramaswamy, Sunnie S. Y. Kim, Olga Russakovsky

Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2021

2020

Deformable Style Transfer.

Sunnie S. Y. Kim, Nicholas I. Kolkin, Jason Salavon, Gregory Shakhnarovich

Proceedings of the Computer Vision - ECCV 2020, 2020