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Sunnie S. Y. Kim
Person information
- affiliation: Princeton University, NJ, USA
- affiliation: Toyota Technological Institute, Chicago, IL, USA
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2020 – today
- 2024
- [c10]Sunnie S. Y. Kim
:
Establishing Appropriate Trust in AI through Transparency and Explainability. CHI Extended Abstracts 2024: 433:1-433:6 - [c9]Upol Ehsan
Human-Centered Explainable AI (HCXAI): Reloading Explainability in the Era of Large Language Models (LLMs). CHI Extended Abstracts 2024: 477:1-477:6 - [c8]Sunnie S. Y. Kim
"I'm Not Sure, But...": Examining the Impact of Large Language Models' Uncertainty Expression on User Reliance and Trust. FAccT 2024: 822-835 - [i14]Giang Nguyen, Mohammad Reza Taesiri, Sunnie S. Y. Kim, Anh Nguyen:
Allowing humans to interactively guide machines where to look does not always improve human-AI team's classification accuracy. CoRR abs/2404.05238 (2024) - [i13]Sunnie S. Y. Kim, Q. Vera Liao, Mihaela Vorvoreanu, Stephanie Ballard, Jennifer Wortman Vaughan:
"I'm Not Sure, But...": Examining the Impact of Large Language Models' Uncertainty Expression on User Reliance and Trust. CoRR abs/2405.00623 (2024) - 2023
- [c7]Sunnie S. Y. Kim, Elizabeth Anne Watkins, Olga Russakovsky, Ruth Fong, Andrés Monroy-Hernández
"Help Me Help the AI": Understanding How Explainability Can Support Human-AI Interaction. CHI 2023: 250:1-250:17 - [c6]Vikram V. Ramaswamy
Overlooked Factors in Concept-Based Explanations: Dataset Choice, Concept Learnability, and Human Capability. CVPR 2023: 10932-10941 - [c5]Sunnie S. Y. Kim
Humans, AI, and Context: Understanding End-Users' Trust in a Real-World Computer Vision Application. FAccT 2023: 77-88 - [i12]Vikram V. Ramaswamy
UFO: A unified method for controlling Understandability and Faithfulness Objectives in concept-based explanations for CNNs. CoRR abs/2303.15632 (2023) - [i11]Sunnie S. Y. Kim, Elizabeth Anne Watkins, Olga Russakovsky, Ruth Fong, Andrés Monroy-Hernández:
Humans, AI, and Context: Understanding End-Users' Trust in a Real-World Computer Vision Application. CoRR abs/2305.08598 (2023) - [i10]Doris Antensteiner, Marah Halawa, Asra Aslam, Ivaxi Sheth, Sachini Herath, Ziqi Huang, Sunnie S. Y. Kim, Aparna Akula, Xin Wang:
WiCV@CVPR2023: The Eleventh Women In Computer Vision Workshop at the Annual CVPR Conference. CoRR abs/2309.12768 (2023) - 2022
- [c4]Sunnie S. Y. Kim
HIVE: Evaluating the Human Interpretability of Visual Explanations. ECCV (12) 2022: 280-298 - [i9]Vikram V. Ramaswamy
ELUDE: Generating interpretable explanations via a decomposition into labelled and unlabelled features. CoRR abs/2206.07690 (2022) - [i8]Vikram V. Ramaswamy
Overlooked factors in concept-based explanations: Dataset choice, concept salience, and human capability. CoRR abs/2207.09615 (2022) - [i7]Sunnie S. Y. Kim, Elizabeth Anne Watkins, Olga Russakovsky, Ruth Fong, Andrés Monroy-Hernández:
"Help Me Help the AI": Understanding How Explainability Can Support Human-AI Interaction. CoRR abs/2210.03735 (2022) - 2021
- [c3]Pedro Savarese, Sunnie S. Y. Kim, Michael Maire, Greg Shakhnarovich, David McAllester:
Information-Theoretic Segmentation by Inpainting Error Maximization. CVPR 2021: 4029-4039 - [c2]Vikram V. Ramaswamy
Fair Attribute Classification Through Latent Space De-Biasing. CVPR 2021: 9301-9310 - [i6]Sunnie S. Y. Kim, Sharon Zhang, Nicole Meister, Olga Russakovsky:
[Re] Don't Judge an Object by Its Context: Learning to Overcome Contextual Bias. CoRR abs/2104.13582 (2021) - [i5]Vivien Nguyen, Sunnie S. Y. Kim:
Cleaning and Structuring the Label Space of the iMet Collection 2020. CoRR abs/2106.00815 (2021) - [i4]Sunnie S. Y. Kim, Nicole Meister, Vikram V. Ramaswamy, Ruth Fong, Olga Russakovsky:
HIVE: Evaluating the Human Interpretability of Visual Explanations. CoRR abs/2112.03184 (2021) - 2020
- [c1]Sunnie S. Y. Kim
Deformable Style Transfer. ECCV (26) 2020: 246-261 - [i3]Sunnie S. Y. Kim, Nicholas I. Kolkin, Jason Salavon
Deformable Style Transfer. CoRR abs/2003.11038 (2020) - [i2]Vikram V. Ramaswamy, Sunnie S. Y. Kim, Olga Russakovsky:
Fair Attribute Classification through Latent Space De-biasing. CoRR abs/2012.01469 (2020) - [i1]Pedro Savarese, Sunnie S. Y. Kim, Michael Maire, Greg Shakhnarovich, David McAllester:
Information-Theoretic Segmentation by Inpainting Error Maximization. CoRR abs/2012.07287 (2020)
Coauthor Index
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last updated on 2024-10-07 21:22 CEST by the dblp team
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