Artificial Intelligence technologies are increasingly used to make decisions and perform autonomous tasks in critical domains. The need to understand AI in order to improve, contest, develop appropriate trust and better interact with AI systems has spurred great academic and public interest in Explainable AI (XAI). The technical field of XAI has produced a vast collection of algorithms in recent years. However, explainability is an inherently human-centric property and the field is starting to embrace human-centered approaches. Human-computer interaction (HCI) research and user experience (UX) design in this area are increasingly important especially as practitioners begin to leverage XAI algorithms to build XAI applications. In this talk, I will draw on our research and broad HCI work to highlight the central role that human-centered approaches should play in shaping XAI technologies, including to drive technical choices by understanding user needs, to uncover pitfalls of existing XAI methods, and to provide conceptual frameworks for human-compatible XAI.