As machine learning becomes increasingly important in everyday life, researchers have examined its relationship to people and society to answer calls for more responsible uses of data-driven technologies. Much work has focused on fairness, accountability, and transparency as well as on explanation and interpretability. However, these terms have resisted definition by computer scientists: while many definitions of each have been put forward, several capturing natural intuitions, these definitions do not capture everything that is meant by associated concept, causing friction with other disciplines and the public. Worse, sometimes different properties conflict explicitly or cannot be satisfied simultaneously. Drawing on our research on the meanings of these terms and the concepts they refer to across different disciplines (e.g., computer science, statistics, public policy, law, social sciences, philosophy, humanities, and others), we present common misconceptions machine learning researchers and practitioners hold when thinking about these topics. For example, it is often axiomatic that producing machine learning explanations automatically makes the outputs of a model more understandable, but this is hardly if ever the case. Similarly, defining fairness as a statistical property of the distribution of model outputs ignores the many procedural requirements supporting fairness in policymaking and the operation of the law. We describe how to integrate the rich meanings of these concepts into machine learning research and practice, enabling attendees to engage with disparate communities of research and practice and to recognize when terms are being overloaded, thereby avoiding speaking to people from other disciplines at cross purposes.