While this book may be a bit more easily accessible to the technologically-minded, it is an incredible read for anyone looking to learn more about the technical aspects of fairness, and how to incorporate these into machine learning algorithms. The Ethical Algorithm touches upon many different subfields of computation and how they play a role in algorithmic fairness — including differential privacy, game theory, the Pareto frontier, statistical parity, p-hacking, and algorithmic interpretability. By learning about all of these techniques, the reader gains a comprehensive understanding of the factors that play a role in designing ethical algorithms, and how best to select these factors when designing a machine learning model so as to minimize harm and discrimination. This book is an intriguing, thought-provoking read that forces the reader to think about algorithmic fairness in new and more technical ways. It does not merely state the issue, but rather delves into potential ways to remedy it.