7 citations · 9 across the 5 of their papers we have counts for
5 papers
A Psychological Theory of Explainability
Scott Cheng-Hsin Yang, Tomas Folke, Patrick Shafto
The goal of explainable Artificial Intelligence (XAI) is to generate human-interpretable explanations, but there are no computationally precise theories of how humans interpret AI…
Explainable AI for Natural Adversarial Images
Tomas Folke, ZhaoBin Li, Ravi B. Sojitra +2
Adversarial images highlight how vulnerable modern image classifiers are to perturbations outside of their training set. Human oversight might mitigate this weakness, but depends o…
Explainable AI for medical imaging: Explaining pneumothorax diagnoses with Bayesian Teaching
Tomas Folke, Scott Cheng-Hsin Yang, Sean Anderson +1
Limited expert time is a key bottleneck in medical imaging. Due to advances in image classification, AI can now serve as decision-support for medical experts, with the potential fo…
Abstraction, Validation, and Generalization for Explainable Artificial Intelligence
Scott Cheng-Hsin Yang, Tomas Folke, Patrick Shafto
Neural network architectures are achieving superhuman performance on an expanding range of tasks. To effectively and safely deploy these systems, their decision-making must be unde…
Mitigating belief projection in explainable artificial intelligence via Bayesian Teaching
Scott Cheng-Hsin Yang, Wai Keen Vong, Ravi B. Sojitra +2
State-of-the-art deep-learning systems use decision rules that are challenging for humans to model. Explainable AI (XAI) attempts to improve human understanding but rarely accounts…