Weapons of Math Destruction
Whether you’re fluent in Java or have never once written a line of code, you will have a hard time putting this book down. O’Neil is conscious of the fact that those interested in the ethics of Big Data come from all fields and backgrounds, and carefully crafts her novel such that all people can gain something from reading it. Weapons of Math Destruction is structured in such a way that each chapter delves into a different area in which Big Data and pernicious algorithms are causing destruction (hence the title), from college admissions to landing credit to so many more. This is a great read for those who are interested in the field of ethical technology, but who either do not possess a technical background or are just starting to get involved in the field.
The Ethical Algorithm
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.