Across a multitude of domains and applications, machine learning has become widespread as a tool for informing decisions about humans, and for humans. But most tools used in practice focus exclusively on mapping inputs to relevant outputs - and take no account of how humans respond to these outputs. This begs the question: how should we design learning systems when we know they will be used in social settings? The goal of this talk is to initiate discussion regarding this question and the paths we can take towards possible answers. Building on strategic classification as an appropriate first step, I will describe some of our work, both recent and current, that aims to extend strategic classification towards more realistic strategic settings that include more elaborate forms of economic modeling. Finally, I will argue for a broader view of how we can approach learning problems that lie just outside the scope of classic supervised learning.