  In recent years, the field of explainable AI (XAI) has produced a vast
collection of algorithms, providing a useful toolbox for researchers and
practitioners to build XAI applications. With the rich application
opportunities, explainability is believed to have moved beyond a demand by data
scientists or researchers to comprehend the models they develop, to an
essential requirement for people to trust and adopt AI deployed in numerous
domains. 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
becoming increasingly important. In this chapter, we begin with a high-level
overview of the technical landscape of XAI algorithms, then selectively survey
our own and other recent HCI works that take human-centered approaches to
design, evaluate, and provide conceptual and methodological tools for XAI. We
ask the question "what are human-centered approaches doing for XAI" and
highlight three roles that they play in shaping XAI technologies by helping
navigate, assess and expand the XAI toolbox: to drive technical choices by
users' explainability needs, to uncover pitfalls of existing XAI methods and
inform new methods, and to provide conceptual frameworks for human-compatible
XAI.
