Explanations--a form of post-hoc interpretability--play an instrumental role\nin making systems accessible as AI continues to proliferate complex and\nsensitive sociotechnical systems. In this paper, we introduce Human-centered\nExplainable AI (HCXAI) as an approach that puts the human at the center of\ntechnology design. It develops a holistic understanding of "who" the human is\nby considering the interplay of values, interpersonal dynamics, and the\nsocially situated nature of AI systems. In particular, we advocate for a\nreflective sociotechnical approach. We illustrate HCXAI through a case study of\nan explanation system for non-technical end-users that shows how technical\nadvancements and the understanding of human factors co-evolve. Building on the\ncase study, we lay out open research questions pertaining to further refining\nour understanding of "who" the human is and extending beyond 1-to-1\nhuman-computer interactions. Finally, we propose that a reflective HCXAI\nparadigm-mediated through the perspective of Critical Technical Practice and\nsupplemented with strategies from HCI, such as value-sensitive design and\nparticipatory design--not only helps us understand our intellectual blind\nspots, but it can also open up new design and research spaces.\n