Equity and diversity are increasingly being recognized as critical areas for attention in human-centered design work. For the development of human-centered AI, they are not only important considerations in AI data and systems design processes but also in relation to the people who design, evaluate and use these systems. Thus, this chapter moves between three layers: (1) data; (2) design; and (3) implementation to explore the detrimental effects of AI systems in which humans have not been at the center. It then discusses how the continued implementation of biased and unfair systems will significantly impact the perceived moral legitimacy of companies and governments as well as AI systems. The chapter makes an argument for the positive impact of HCAI as a universal approach to improve gender representation, diversity, and fairness in AI, with the added benefit of unforeseeable gains achieved by widening the aperture to look at how humans are moving through the world. The chapter discusses reframing notions of “implementation” to include ongoing and engaged monitoring and maintenance of AI systems and the systems that embody them. It offers strategies for exploring the social contexts within which AI systems are intended to be used, building diversity into work teams, and engaging with communities to identify and mitigate potentially detrimental impacts of AI systems. The chapter draws attention to the challenges for putting such strategies in place, with suggestions for future directions. It closes with recommendations for building diversity into teams and applying inclusive practices both in the workplace and when engaging with communities.