All AI systems make mistakes, but users often struggle to identify them. This can lead to the problem of overreliance on AI—users accepting incorrect AI outputs—that causes negative consequences, such as poor human-AI team performance and ineffective human oversight. This chapter examines the critical issue of overreliance, particularly overreliance on Generative AI (GenAI), through a comprehensive literature review of 120+ research papers from disciplines such as Computer-Supported Cooperative Work and Social Computing (CSCW); Explainable AI (XAI), Fairness, Accountability, and Transparency (FAccT); Human-Computer Interaction (HCI); Human Factors; Intelligent User Interfaces (IUI); and Organizational Science. The chapter outlines specific antecedents (e.g., AI literacy), mechanisms (e.g., automation bias), and measurements (e.g., weight of advice) of overreliance on AI. The chapter explains four types of existing overreliance mitigation strategies, including cognitive forcing functions and uncertainty expressions. These strategies promote a human-centered approach to AI design, development, and use, helping foster appropriate reliance. The chapter explains the notion of appropriate reliance on AI, its key components, and its strategy-graded approach. The chapter concludes with a short case study and ends by highlighting future trends and research directions.