This paper addresses the steep learning curve in Machine Learning faced by noncomputer scientists, particularly social scientists, stemming from the absence of a primer on its fundamental principles. I adopt a pedagogical strategy inspired by the adage "once you understand OLS, you can work your...

We develop new quasi-experimental tools to understand algorithmic discrimination and build non-discriminatory algorithms when the outcome of interest is only selectively observed. These tools are applied in the context of pretrial bail decisions, where conventional algorithmic predictions are...