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Brittle AI, Causal Confusion, and Bad Mental Models: Challenges and Successes in the XAI Program
The advances in artificial intelligence enabled by deep learning architectures are undeniable.
Explainable Artificial Intelligence (XAI) for Increasing User Trust in Deep Reinforcement Learning Driven Autonomous Systems
We consider the problem of providing users of deep Reinforcement Learning (RL) based systems with a better understanding of when their output can be trusted.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
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Improving the Performance of Fine-Grain Image Classifiers via Generative Data Augmentation
Recent advances in machine learning (ML) and computer vision tools have enabled applications in a wide variety of arenas such as financial analytics, medical diagnostics, and even within the Department of Defense.
Privacy preserving Neural Network Inference on Encrypted Data with GPUs
Machine Learning as a Service (MLaaS) has become a growing trend in recent years and several such services are currently offered.
Causal Learning and Explanation of Deep Neural Networks via Autoencoded Activations
We develop an approach to explaining deep neural networks by constructing causal models on salient concepts contained in a CNN.
