A tour of conformal prediction: regression, classification, and equitable treatment Modern machine learning algorithms have achieved remarkable performance in a myriad of applications, and are increasingly used to make impactful decisions in the hiring process, criminal sentencing, and healthcare diagnostics. The use of data-driven algorithms in high-stakes applications is exciting yet alarming: these methods are extremely complex, often brittle, notoriously hard to analyze and interpret. Naturally, concerns have been raised about the reliability of the output of such algorithms. This talk introduces statistical tools that can be wrapped around any "black-box" algorithm to provide valid inferential results while taking advantage of their impressive performance. We present novel developments in conformal prediction, which rigorously guarantee the reliability of complex predictive models both in regression and classification tasks, and, if time permits, show how these methodologies can be used to treat individuals equitably.