Explainability is an essential pillar of Responsible AI that calls for equitable and ethical Human-AI interaction. Explanations are essential to hold AI systems and their producers accountable, and can serve as a means to ensure humans' right to understand and contest AI decisions. Human-centered XAI (HCXAI) argues that there is more to making AI explainable than algorithmic transparency. Explainability of AI is more than just "opening"the black box - who opens it matters just as much, if not more, as the ways of opening it. In this third CHI workshop on Human-centered XAI (HCXAI), we build on the maturation through the first two installments to craft the coming-of-age story of HCXAI, which embodies a deeper discourse around operationalizing human-centered perspectives in XAI. We aim towards actionable interventions that recognize both affordances and potential pitfalls of XAI. The goal of the third installment is to go beyond the black box and examine how human-centered perspectives in XAI can be operationalized at the conceptual, methodological, and technical levels. Encouraging holistic (historical, sociological, and technical) approaches, we emphasize "operationalizing."Within our research agenda for XAI, we seek actionable analysis frameworks, concrete design guidelines, transferable evaluation methods, and principles for accountability.