This report introduces four principles for explainable artificial intelligence (XAI) that comprise the fundamental properties for explainable AI systems. They were developed to encompass the multidisciplinary nature of explainable AI, including the fields of computer science, engineering, and psychology. Because one size fits all explanations do not exist, different users will require different types of explanations. This report presents five categories of explanation and summarize theories of explainable AI. It gives an overview of the algorithms in the field that cover the major classes of explainable algorithms. As a baseline comparison, we assess how well explanations provided by people follow our four principles. This assessment provides insights to the challenges of designing explainable AI systems.