Fong, Ruth

Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claims

Brundage, M., Avin, S., Wang, J., Belfield, H., Krueger, G., Hadfield, G., Khlaaf, H., Yang, J., Toner, H., Fong, R., et al. (2020) · arXiv

Ethics and Social Impacts of AISafety Systems Engineering in Autonomy

Cited by 306

Help Me Help the AI": Understanding How Explainability Can Support Human-AI Interaction

Kim, S. S. Y., Watkins, E. A., Russakovsky, O., Fong, R., & Monroy-Hernández, A. (2023) · Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems

Explainable Artificial Intelligence (XAI)Usability and User Interface Design

Cited by 233

HIVE: Evaluating the Human Interpretability of Visual Explanations

Kim, S. S. Y., Meister, N., Ramaswamy, V. V., Fong, R., & Russakovsky, O. (2022) · Lecture Notes in Computer Science

Explainable Artificial Intelligence (XAI)Advanced Neural Network Applications

Cited by 72

Humans, AI, and Context: Understanding End-Users’ Trust in a Real-World Computer Vision Application

Kim, S. S. Y., Watkins, E. A., Russakovsky, O., Fong, R., & Monroy-Hernández, A. (2023) · 2023 ACM Conference on Fairness, Accountability, and Transparency

Human-Automation Interaction and SafetyExplainable Artificial Intelligence (XAI)

Cited by 37