Interactive Explanations of Neural Networks and Artificial Intelligence (Int-XAI)
Deep learning architectures have become synonymous with state-of-the-art performance across a broad spectrum of domains. In everything from natural language processing and generation for conversation, to machine vision for clinical decision support, intelligent systems are supporting both the personal and professional spheres of our society. Explaining the outcomes and decision-making of these systems remains a challenge. As the prevalence of AI grows in our society, so too does the complexity and expectation surrounding the ability of autonomous models to explain their actions.