The use of AI comes with inherent risks to humans and therefore impacts their trust in such systems. These include, but are not limited to, technical, ethical, privacy, security, social, and legal risks. Each of these impacts the ability of a human to properly calibrate their trust in an AI system. From a human-centered AI (HCAI) perspective, building appropriate trust requires designing AI systems that align with human needs, values, and capabilities. The present chapter examines and evaluates the foundational concepts of human trust in AI, reviews the major risks to trust, discusses the trust calibration process through transparency, interpretability, and user-centered design, and summarizes how explainability impacts trust in AI. It also elucidates ethical considerations that impact human trust in AI as well evaluates the regulatory and legal viewpoints that can and should exist to govern AI. It provides a window upon the future trends and challenges of AI governance and regulation to create trustworthy systems. Finally, it asserts that human trust in AI is predicated upon the understanding and acceptance of the risk of the use of AI, and without the opportunity for a choice whether to use the technology or not, the issue of trust becomes sadly inconsequential.