The tutorial aims to familiarize the ML community with major existing AI governance frameworks, ongoing AI policy proposals worldwide, and the concrete tools the research community has developed to adhere to standards and regulations applicable to ML systems in socially high-stakes domains. As a concrete governance challenge, we will focus on issues of bias and unfairness and overview pipeline-centric approaches to operationalize algorithmic harm prevention. As we will discuss, this approach is particularly relevant to challenges around leveraging the disparate impact doctrine for algorithmic harm prevention and recent FTC advanced notice of proposed rulemakings (ANPRMs). The concluding expert panel is an opportunity for the ML community to hear diverse perspectives on the key AI governance challenges in the near future and how the ML research community can prepare for and support efforts to address those challenges.