Unconscious Bias in Schools


Book Description

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 for Young Children and Ourselves


Book Description

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




Biased


Book Description

"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.




Big Data and Social Science


Book Description

Both Traditional Students and Working Professionals Acquire the Skills to Analyze Social Problems. Big Data and Social Science: A Practical Guide to Methods and Tools shows how to apply data science to real-world problems in both research and the practice. The book provides practical guidance on combining methods and tools from computer science, statistics, and social science. This concrete approach is illustrated throughout using an important national problem, the quantitative study of innovation. The text draws on the expertise of prominent leaders in statistics, the social sciences, data science, and computer science to teach students how to use modern social science research principles as well as the best analytical and computational tools. It uses a real-world challenge to introduce how these tools are used to identify and capture appropriate data, apply data science models and tools to that data, and recognize and respond to data errors and limitations. For more information, including sample chapters and news, please visit the author's website.




Algorithms of Oppression


Book Description

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




Implicit Bias in Schools


Book Description

Implicit bias is often recognized as one of the reasons for instances of discrimination and injustice, despite most people explicitly believing in the importance of equality and justice for all people. Implicit Bias in Schools provides practitioners with an understanding of implicit bias and how to address it from start to finish: what it is, how it is a problem, and how we can fix it. Grounded in an accessible summary of research on bias and inequity in schools, this book bridges the research-to-practice gap by exploring how implicit bias affects students and what school leaders can do to mitigate the effects of bias in their schools. Covering issues of discipline, instruction, academic achievement, mindfulness, data collection, and culturally relevant practices, and full of rich examples and strategies, Implicit Bias in Schools is a must-have resource for educators today. Supplemental material, including links to resources mentioned in the text, tools, and worksheets to assist your journey when implementing strategies at your own school can be found at www.routledge.com/9781138497061.




Bias Interrupted


Book Description

A cutting-edge, relentless, objective approach to inclusion. Companies spend billions of dollars annually on diversity efforts with remarkably few results. Too often diversity efforts rest on the assumption that all that's needed is an earnest conversation about "privilege." That's not enough. To truly make progress we need to stop celebrating the problem and instead take effective steps to solve it. In Bias Interrupted, Joan C. Williams shows how it's done, and, reassuringly, how easy it is to get started. One of today's preeminent voices on inclusive workplaces, Williams explains how leaders can use standard business tools—data, metrics, and persistence—to interrupt the bias that is continually transmitted through formal systems like performance appraisals, as well as the informal systems that control access to career-enhancing opportunities. The book presents fresh evidence, based on Williams's exhaustive research and work with companies, that interrupting bias helps every group—including white men. Comprehensive, though compact and straightforward, Bias Interrupted delivers real, practical value in an efficient and accessible manner to an audience that has never needed it more. It's possible to interrupt bias. Here's where you start.




Race After Technology


Book Description

From everyday apps to complex algorithms, Ruha Benjamin cuts through tech-industry hype to understand how emerging technologies can reinforce White supremacy and deepen social inequity. Benjamin argues that automation, far from being a sinister story of racist programmers scheming on the dark web, has the potential to hide, speed up, and deepen discrimination while appearing neutral and even benevolent when compared to the racism of a previous era. Presenting the concept of the “New Jim Code,” she shows how a range of discriminatory designs encode inequity by explicitly amplifying racial hierarchies; by ignoring but thereby replicating social divisions; or by aiming to fix racial bias but ultimately doing quite the opposite. Moreover, she makes a compelling case for race itself as a kind of technology, designed to stratify and sanctify social injustice in the architecture of everyday life. This illuminating guide provides conceptual tools for decoding tech promises with sociologically informed skepticism. In doing so, it challenges us to question not only the technologies we are sold but also the ones we ourselves manufacture. Visit the book's free Discussion Guide: www.dropbox.com




Discrimination at Work


Book Description

This volume brings together top scholars in industrial and organizational psychology with social psychologists to explore the research and theory relating to various areas of workplace discrimination. Many of the contributors to this book participated in a conference on workplace discrimination held at Rice University in May 2000. The idea came from the realization that there had been no attempt to bring together the various literatures on the topic. Discrimination and issues of employment diversity are significant topics today in IO psychology, business, and human resource management. This edited volume examines the following components of this important discussion: how to explain discrimination in organizations; understanding discrimination against specific groups; and implications for practical efforts to reduce discrimination. This book brings together, in one volume, a review of the research on discrimination based on race, age, sexual orientation, gender, physical appearance, disability, and personality. In addition, it explores the multilevel antecedents and potential bases for a general model of discrimination in the workplace. While social psychological research and theory have provided invaluable insights, an understanding of discrimination in the workplace and solutions will require incorporating factors at the organizational level in addition to factors at the individual and group levels. Although a definitive model is not reached, the aim of this text is to facilitate future research and theory.




Understand, Manage, and Prevent Algorithmic Bias


Book Description

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