Over the past few years, fairness has emerged as a matter of serious concern within machine learning. There is growing recognition that even models developed with the best of intentions may exhibit discriminatory biases, perpetuate inequality, or perform less well for historically disadvantaged groups. Considerable work is already underway within and outside machine learning to both characterize and address these problems. This tutorial will take a novel approach to parsing the topic, adopting three perspectives: statistics, causality, and measurement. Each viewpoint will shed light on different facets of the problem and help explain matters of continuing technical and normative debate. Rather than attempting to resolve questions of fairness within a single technical framework, the tutorial aims to equip the audience with a coherent toolkit to critically examine the many ways that machine learning implicates fairness.