Deformable Style Transfer. - Sunnie S. Y. Kim; Nicholas I. Kolkin; Jason Salavon; Gregory Shakhnarovich (ECCV (26), 2020)

Fair Attribute Classification Through Latent Space De-Biasing. - Vikram V. Ramaswamy; Sunnie S. Y. Kim; Olga Russakovsky (CVPR, 2021)

Information-Theoretic Segmentation by Inpainting Error Maximization. - Pedro Savarese; Sunnie S. Y. Kim; Michael Maire; Greg Shakhnarovich; David McAllester (CVPR, 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)

Cleaning and Structuring the Label Space of the iMet Collection 2020. - Vivien Nguyen; Sunnie S. Y. Kim (CoRR, 2021)

HIVE: Evaluating the Human Interpretability of Visual Explanations. - Sunnie S. Y. Kim; Nicole Meister; Vikram V. Ramaswamy; Ruth Fong; Olga Russakovsky (ECCV (12), 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)

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)

"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)

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)

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)

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)

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)

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 Extended Abstracts, 2024)

Establishing Appropriate Trust in AI through Transparency and Explainability. - Sunnie S. Y. Kim (CHI Extended Abstracts, 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, 2024)

Allowing Humans to Interactively Guide Machines Where to Look Does Not Always Improve Human-AI Team's Classification Accuracy. - Giang Nguyen 004; Mohammad Reza Taesiri; Sunnie S. Y. Kim; Anh Totti Nguyen (XAI4CV, 2024)

Portraying Large Language Models as Machines, Tools, or Companions Affects What Mental Capacities Humans Attribute to Them. - Allison Chen; Sunnie S. Y. Kim; Amaya Dharmasiri; Olga Russakovsky; Judith E. Fan (CHI Extended Abstracts, 2025)

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)

Interactivity x Explainability: Toward Understanding How Interactivity Can Improve Computer Vision Explanations. - Indu Panigrahi; Sunnie S. Y. Kim; Amna Liaqat; Rohan Jinturkar; Olga Russakovsky; Ruth Fong; Parastoo Abtahi (CHI Extended Abstracts, 2025)