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"Poignant....important and illuminating."—The New York Times Book Review "Groundbreaking."—Bryan Stevenson, New York Times bestselling author of Just Mercy From one of the world’s leading experts on unconscious racial bias come stories, science, and strategies to address one of the central controversies of our time How do we talk about bias? How do we address racial disparities and inequities? What role do our institutions play in creating, maintaining, and magnifying those inequities? What role do we play? With a perspective that is at once scientific, investigative, and informed by personal experience, Dr. Jennifer Eberhardt offers us the language and courage we need to face one of the biggest and most troubling issues of our time. She exposes racial bias at all levels of society—in our neighborhoods, schools, workplaces, and criminal justice system. Yet she also offers us tools to address it. Eberhardt shows us how we can be vulnerable to bias but not doomed to live under its grip. Racial bias is a problem that we all have a role to play in solving.
A Source Book for Mediæval History is a scholarly piece by Oliver J. Thatcher. It covers all major historical events and leaders from the Germania of Tacitus in the 1st century to the decrees of the Hanseatic League in the 13th century.
A concise and self-contained introduction to causal inference, increasingly important in data science and machine learning. The mathematization of causality is a relatively recent development, and has become increasingly important in data science and machine learning. This book offers a self-contained and concise introduction to causal models and how to learn them from data. After explaining the need for causal models and discussing some of the principles underlying causal inference, the book teaches readers how to use causal models: how to compute intervention distributions, how to infer causal models from observational and interventional data, and how causal ideas could be exploited for classical machine learning problems. All of these topics are discussed first in terms of two variables and then in the more general multivariate case. The bivariate case turns out to be a particularly hard problem for causal learning because there are no conditional independences as used by classical methods for solving multivariate cases. The authors consider analyzing statistical asymmetries between cause and effect to be highly instructive, and they report on their decade of intensive research into this problem. The book is accessible to readers with a background in machine learning or statistics, and can be used in graduate courses or as a reference for researchers. The text includes code snippets that can be copied and pasted, exercises, and an appendix with a summary of the most important technical concepts.