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H.B. Keller Colloquium

Monday, January 13, 2020
4:00pm to 5:00pm
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Annenberg 105
Explainable AI: Making Visual Question Answering Systems More Transparent
Raymond Mooney, Professor, Department of Computer Science, University of Texas at Austin,

Artificial Intelligence systems' ability to explain their conclusions is crucial to their utility and trustworthiness. Deep neural networks have enabled significant progress on many challenging problems such as visual question answering (VQA), the task of answering natural language questions about images. However, most of them are opaque black boxes with limited explanatory capability. The goal of Explainable AI is to increase the transparency of complex AI systems such as deep networks. We have developed a novel approach to XAI and used it to build a high-performing VQA system that can elucidate its answers with multi-modal natural-language and visual explanations that faithfully reflect important aspects of its underlying reasoning while capturing the style of comprehensible human explanations. Crowd-sourced human evaluation of these explanations demonstrate the advantages of our approach.

For more information, please contact Diana Bohler by phone at 6263951768 or by email at dbohler@caltech.edu.