| 2026 | SIGCSE | Characterizing the Relationship Between Generative AI, Student Behavior, and Learning Outcomes in Upper-Level CS Education: A Case Study in an Undergraduate Machine Learning Course. | Anha Khan, Romina Mahinpei, Maryam Hedayati, Victoria Dean, Ruth Fong |
| 2025 | CHI | 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 |
| 2023 | CHI | "Help Me Help the AI": Understanding How Explainability Can Support Human-AI Interaction. | Sunnie S. Y. Kim, Elizabeth Anne Watkins, Olga Russakovsky, Ruth Fong, Andrs Monroy-Hernndez |
| 2023 | CVPR | Improving Data-Efficient Fossil Segmentation via Model Editing. | Indu Panigrahi, Ryan Manzuk, Adam Maloof, Ruth Fong |
| 2023 | CVPR | Overlooked Factors in Concept-Based Explanations: Dataset Choice, Concept Learnability, and Human Capability. | Vikram V. Ramaswamy, Sunnie S. Y. Kim, Ruth Fong, Olga Russakovsky |
| 2023 | ICCV | Gender Artifacts in Visual Datasets. | Nicole Meister, Dora Zhao, Angelina Wang, Vikram V. Ramaswamy, Ruth Fong, Olga Russakovsky |
| 2022 | ECCV | HIVE: Evaluating the Human Interpretability of Visual Explanations. | Sunnie S. Y. Kim, Nicole Meister, Vikram V. Ramaswamy, Ruth Fong, Olga Russakovsky |
| 2021 | ICCV | On Compositions of Transformations in Contrastive Self-Supervised Learning. | Mandela Patrick, Yuki Markus Asano, Polina Kuznetsova, Ruth Fong, Joo F. Henriques, Geoffrey Zweig, Andrea Vedaldi |
| 2020 | ACCV | Contextual Semantic Interpretability. | Diego Marcos, Ruth Fong, Sylvain Lobry, Rmi Flamary, Nicolas Courty, Devis Tuia |
| 2020 | CVPR | There and Back Again: Revisiting Backpropagation Saliency Methods. | Sylvestre-Alvise Rebuffi, Ruth Fong, Xu Ji, Andrea Vedaldi |
| 2020 | ICML | xxAI - Beyond Explainable Artificial Intelligence. | Andreas Holzinger, Randy Goebel, Ruth Fong, Taesup Moon, Klaus-Robert Mller, Wojciech Samek |
| 2019 | ICCV | Understanding Deep Networks via Extremal Perturbations and Smooth Masks. | Ruth Fong, Mandela Patrick, Andrea Vedaldi |
| 2018 | CVPR | Net2Vec: Quantifying and Explaining How Concepts Are Encoded by Filters in Deep Neural Networks. | Ruth Fong, Andrea Vedaldi |