We’ve seen a lot of talks, frameworks, and guidelines over the past year on regulating ML. In this talk, I’ll discuss why the current norms and practices in the ML research and practice community don’t lend themselves well to being regulated and what types of changes need to take place, from scoping and formulation, to design and development, to evaluation and monitoring, in order to result in ML systems that could be reliably deployed to help achieve societal, business, and policy outcomes that we want.