In Unconscious Bias in Schools, two seasoned educators describe the phenomenon of unconscious racial bias and how it negatively affects the work of educators and students in schools. “Regardless of the amount of effort, time, and resources education leaders put into improving the academic achievement of students of color,” the authors write, “if unconscious racial bias is overlooked, improvement efforts may never achieve their highest potential.” In order to address this bias, the authors argue, educators must first be aware of the racialized context in which we live. Through personal anecdotes and real-life scenarios, Unconscious Bias in Schools provides education leaders with an essential roadmap for addressing these issues directly. The authors draw on the literature on change management, leadership, critical race theory, and racial identity development, as well as the growing research on unconscious bias in a variety of fields, to provide guidance for creating the conditions necessary to do this work—awareness, trust, and a “learner’s stance.” Benson and Fiarman also outline specific steps toward normalizing conversations about race; reducing the influence of bias on decision-making; building empathic relationships; and developing a system of accountability. All too often, conversations about race become mired in questions of attitude or intention–“But I’m not a racist!” This book shows how information about unconscious bias can help shift conversations among educators to a more productive, collegial approach that has the potential to disrupt the patterns of perception that perpetuate racism and institutional injustice. Tracey A. Benson is an assistant professor of educational leadership at the University of North Carolina at Charlotte. Sarah E. Fiarman is the director of leadership development for EL Education, and a former public school teacher, principal, and lecturer at Harvard Graduate School of Education.

Anti-bias education begins with you! Become a skilled anti-bias teacher with this practical guidance to confronting and eliminating barriers.

Focusing on seven case studies of secondary schools in India, Malaysia, Nigeria, Pakistan, Samoa, Seychelles, and Trinidad & Tobago, this book analyses whether schools perpetuate gender stereotypes and investigates how this can be prevented. It provides insights and recommendations useful for policy-makers and educators worldwide.

"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.

Acknowledgments -- Introduction: the power of algorithms -- A society, searching -- Searching for Black girls -- Searching for people and communities -- Searching for protections from search engines -- The future of knowledge in the public -- The future of information culture -- Conclusion: algorithms of oppression -- Epilogue -- Notes -- Bibliography -- Index -- About the author

What information should jurors have during court proceedings to render a just decision? Should politicians know who is donating money to their campaigns? Will scientists draw biased conclusions about drug efficacy when they know more about the patient or study population? The potential for bias in decision-making by physicians, lawyers, politicians, and scientists has been recognized for hundreds of years and drawn attention from media and scholars seeking to understand the role that conflicts of interests and other psychological processes play. However, commonly proposed solutions to biased decision-making, such as transparency (disclosing conflicts) or exclusion (avoiding conflicts) do not directly solve the underlying problem of bias and may have unintended consequences. Robertson and Kesselheim bring together a renowned group of interdisciplinary scholars to consider another way to reduce the risk of biased decision-making: blinding. What are the advantages and limitations of blinding? How can we quantify the biases in unblinded research? Can we develop new ways to blind decision-makers? What are the ethical problems with withholding information from decision-makers in the course of blinding? How can blinding be adapted to legal and scientific procedures and in institutions not previously open to this approach? Fundamentally, these sorts of questions—about who needs to know what—open new doors of inquiry for the design of scientific research studies, regulatory institutions, and courts. The volume surveys the theory, practice, and future of blinding, drawing upon leading authors with a diverse range of methodologies and areas of expertise, including forensic sciences, medicine, law, philosophy, economics, psychology, sociology, and statistics. - Introduces readers to the primary policy issue this book seeks to address: biased decision-making. - Provides a focus on blinding as a solution to bias, which has applicability in many domains. - Traces the development of blinding as a solution to bias, and explores the different ways blinding has been employed. - Includes case studies to explore particular uses of blinding for statisticians, radiologists, and fingerprint examiners, and whether the jurors and judges who rely upon them will value and understand blinding.

Are algorithms friend or foe? The human mind is evolutionarily designed to take shortcuts in order to survive. We jump to conclusions because our brains want to keep us safe. A majority of our biases work in our favor, such as when we feel a car speeding in our direction is dangerous and we instantly move, or when we decide not take a bite of food that appears to have gone bad. However, inherent bias negatively affects work environments and the decision-making surrounding our communities. While the creation of algorithms and machine learning attempts to eliminate bias, they are, after all, created by human beings, and thus are susceptible to what we call algorithmic bias. In Understand, Manage, and Prevent Algorithmic Bias, author Tobias Baer helps you understand where algorithmic bias comes from, how to manage it as a business user or regulator, and how data science can prevent bias from entering statistical algorithms. Baer expertly addresses some of the 100+ varieties of natural bias such as confirmation bias, stability bias, pattern-recognition bias, and many others. Algorithmic bias mirrors—and originates in—these human tendencies. Baer dives into topics as diverse as anomaly detection, hybrid model structures, and self-improving machine learning. While most writings on algorithmic bias focus on the dangers, the core of this positive, fun book points toward a path where bias is kept at bay and even eliminated. You’ll come away with managerial techniques to develop unbiased algorithms, the ability to detect bias more quickly, and knowledge to create unbiased data. Understand, Manage, and Prevent Algorithmic Bias is an innovative, timely, and important book that belongs on your shelf. Whether you are a seasoned business executive, a data scientist, or simply an enthusiast, now is a crucial time to be educated about the impact of algorithmic bias on society and take an active role in fighting bias. What You'll Learn Study the many sources of algorithmic bias, including cognitive biases in the real world, biased data, and statistical artifact Understand the risks of algorithmic biases, how to detect them, and managerial techniques to prevent or manage them Appreciate how machine learning both introduces new sources of algorithmic bias and can be a part of a solutionBe familiar with specific statistical techniques a data scientist can use to detect and overcome algorithmic bias Who This Book is For Business executives of companies using algorithms in daily operations; data scientists (from students to seasoned practitioners) developing algorithms; compliance officials concerned about algorithmic bias; politicians, journalists, and philosophers thinking about algorithmic bias in terms of its impact on society and possible regulatory responses; and consumers concerned about how they might be affected by algorithmic bias