Assess the feasibility of a novel technology which automatically generates human-understandable explanations of an artificial intelligence (AI) algorithms decisionsfor example, a deep neural networks (DNNs) classification decisions. Our technology, called EMERALD (Explainable Machine Reasoning through the Application of Linked Data), is designed to improve trust and transparency within human-in-the-loop AI systems by providing the human operator with an informed basis for accepting/rejecting/modifying the decisions of the AI. EMERALD works by leveraging background information contained within knowledge graphs to infer an AIs internal model of the world based on its external behaviors. EMERALD is designed to take advantage of ever-increasing volumes of structured semantic information (linked data) available on the Web. The recent proliferation of explicitly structured Semantic Web data (two prominent examples being Wikidata and the Google Knowledge Graph) has occurred at the same time as powerful black box machine learning algorithms such as DNNs have become ubiquitous. Because these algorithms often encode information implicitly, their thought processes defy easy interpretation. With EMERALD, we will capitalize on one AI trend (knowledge graphs) to improve the transparency and trustworthiness of another (machine learning). As such, our approach resides at the vanguard of so-called third-wave contextual AI Research.FA-002,Explainable Artificial Intelligence,machine learning algorithms,machine reasoning,human-machine teaming,Intelligence Surveillance & Reconnaissance,Third-Wave AI
