Search results for: weapons-of-math-destruction

Weapons of Math Destruction

Author : Cathy O'Neil
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NEW YORK TIMES BESTSELLER • A former Wall Street quant sounds the alarm on Big Data and the mathematical models that threaten to rip apart our social fabric—with a new afterword “A manual for the twenty-first-century citizen . . . relevant and urgent.”—Financial Times NATIONAL BOOK AWARD LONGLIST • NAMED ONE OF THE BEST BOOKS OF THE YEAR BY The New York Times Book Review • The Boston Globe • Wired • Fortune • Kirkus Reviews • The Guardian • Nature • On Point We live in the age of the algorithm. Increasingly, the decisions that affect our lives—where we go to school, whether we can get a job or a loan, how much we pay for health insurance—are being made not by humans, but by machines. In theory, this should lead to greater fairness: Everyone is judged according to the same rules. But as mathematician and data scientist Cathy O’Neil reveals, the mathematical models being used today are unregulated and uncontestable, even when they’re wrong. Most troubling, they reinforce discrimination—propping up the lucky, punishing the downtrodden, and undermining our democracy in the process. Welcome to the dark side of Big Data.

Summary of Cathy O Neil s Weapons of Math Destruction

Author : Milkyway Media
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Buy now to get the main key ideas from Cathy O'Neil's Weapons of Math Destruction Technological advances are often lauded as unbiased and fair. But in Weapons of Math Destruction (2016), data scientist Cathy O’Neil posits that the opposite is true. Today’s world is largely ruled by mathematical algorithms that decide most aspects of life, from education to work, insurance to elections. Existing models have proven to be harmful. They promote inequalities by favoring the wealthy and the privileged and pushing minorities and poor people further down. These models, by being invisible, unregulated, and indisputable, form Weapons of Math Destruction fueled by the Big Data that could divide the world even more if left unchecked.

SUMMARY Weapons Of Math Destruction How Big Data Increases Inequality And Threatens Democracy By Cathy O Neil

Author : Shortcut Edition
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* Our summary is short, simple and pragmatic. It allows you to have the essential ideas of a big book in less than 30 minutes. By reading this summary, you will discover that mathematical models, and more particularly algorithms coupled with information systems, may increase inequalities and threaten democracies. You will also discover that : mathematical models are not neutral, but hide ideologies and personal interests; algorithms promise efficiency and lowest cost, but increase inequalities and injustices; mathematical formulas affect your life choices; your personal data are weapons used by the giants of Tech. At a time when algorithms are king, the decisions that affect your life - which school to go to, which loan to take out - are no longer made by humans, but by mathematical models. In theory, this should promote fairness: everyone is judged by the same level of value. Mathematician Cathy O'Neil argues the opposite. These opaque, unregulated models can cause irreparable damage, like the mortgage payments of American households during the subprime crisis in 2007. Worse: they accentuate discrimination. For example, a student from a modest background who cannot obtain a loan - too risky - will never have access to quality education. These mathematical models support the lucky ones and disadvantage the oppressed: welcome to the dark side of big data, the exponential growth of digital data! *Buy now the summary of this book for the modest price of a cup of coffee!

Book Review

Author : Apurv Jain
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Cathy O'Neil is a quant, and her experience comes across in her entertaining book “Weapons of Math Destruction.” It is an insider's disillusioned tale about the dark side of large scale predictive analytics. These data and models affect many areas of our life from the financial markets where we invest our savings, to selecting educational institutions, the labor market, our consumer behavior such as obtaining insurance and credit, our civic life, and even the prison system. The author cautions us against the religious fervor with which we, as a society, are embracing quantitative models in our quest for efficiency. She shows how models are biased, often built on flimsy understanding, lack a self-correcting feedback loop and accountability, and tend to create their own reality at the expense of the poor and the vulnerable. The author's demands for more transparency in algorithms and the sacrificing of mathematical efficiency in the interest of fairness as a society should be a catalyst for important policy conversations. The title of the book might be an homage to Warren Buffet's 2002 Berkshire annual report where citing flawed models and counterparty concentration risk (among others) he famously called derivatives, financial weapons of mass destruction.

A Joosr Guide to Weapons of Math Destruction by Cathy O Neil

Author : Joosr
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The Shame Machine

Author : Cathy O'Neil
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NEW YORK TIMES EDITORS’ CHOICE • A clear-eyed warning about the increasingly destructive influence of America’s “shame industrial complex” in the age of social media and hyperpartisan politics—from the New York Times bestselling author of Weapons of Math Destruction “O’Neil reminds us that we must resist the urge to judge, belittle, and oversimplify, and instead allow always for complexity and lead always with empathy.”—Dave Eggers, author of The Every Shame is a powerful and sometimes useful tool: When we publicly shame corrupt politicians, abusive celebrities, or predatory corporations, we reinforce values of fairness and justice. But as Cathy O’Neil argues in this revelatory book, shaming has taken a new and dangerous turn. It is increasingly being weaponized—used as a way to shift responsibility for social problems from institutions to individuals. Shaming children for not being able to afford school lunches or adults for not being able to find work lets us off the hook as a society. After all, why pay higher taxes to fund programs for people who are fundamentally unworthy? O’Neil explores the machinery behind all this shame, showing how governments, corporations, and the healthcare system capitalize on it. There are damning stories of rehab clinics, reentry programs, drug and diet companies, and social media platforms—all of which profit from “punching down” on the vulnerable. Woven throughout The Shame Machine is the story of O’Neil’s own struggle with body image and her recent weight-loss surgery, which awakened her to the systematic shaming of fat people seeking medical care. With clarity and nuance, O’Neil dissects the relationship between shame and power. Whom does the system serve? Is it counter-productive to call out racists, misogynists, and vaccine skeptics? If so, when should someone be “canceled”? How do current incentive structures perpetuate the shaming cycle? And, most important, how can we all fight back?

Discriminating Data

Author : Wendy Hui Kyong Chun
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How big data and machine learning encode discrimination and create agitated clusters of comforting rage. In Discriminating Data, Wendy Hui Kyong Chun reveals how polarization is a goal—not an error—within big data and machine learning. These methods, she argues, encode segregation, eugenics, and identity politics through their default assumptions and conditions. Correlation, which grounds big data’s predictive potential, stems from twentieth-century eugenic attempts to “breed” a better future. Recommender systems foster angry clusters of sameness through homophily. Users are “trained” to become authentically predictable via a politics and technology of recognition. Machine learning and data analytics thus seek to disrupt the future by making disruption impossible. Chun, who has a background in systems design engineering as well as media studies and cultural theory, explains that although machine learning algorithms may not officially include race as a category, they embed whiteness as a default. Facial recognition technology, for example, relies on the faces of Hollywood celebrities and university undergraduates—groups not famous for their diversity. Homophily emerged as a concept to describe white U.S. resident attitudes to living in biracial yet segregated public housing. Predictive policing technology deploys models trained on studies of predominantly underserved neighborhoods. Trained on selected and often discriminatory or dirty data, these algorithms are only validated if they mirror this data. How can we release ourselves from the vice-like grip of discriminatory data? Chun calls for alternative algorithms, defaults, and interdisciplinary coalitions in order to desegregate networks and foster a more democratic big data.

Weapons of Math Destruction

Author : Mathematician Publishing
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This journal is a perfect gift for friends and family, male or female. Other features of this notebook are: - 120 pages - 6x9 inches - matte cover This book is convenient for writing. It has the perfect size to carry anywhere for journaling and note taking.

Composition and Big Data

Author : Amanda Licastro
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In a data-driven world, anything can be data. As the techniques and scale of data analysis advance, the need for a response from rhetoric and composition grows ever more pronounced. It is increasingly possible to examine thousands of documents and peer-review comments, labor-hours, and citation networks in composition courses and beyond. Composition and Big Data brings together a range of scholars, teachers, and administrators already working with big-data methods and datasets to kickstart a collective reckoning with the role that algorithmic and computational approaches can, or should, play in research and teaching in the field. Their work takes place in various contexts, including programmatic assessment, first-year pedagogy, stylistics, and learning transfer across the curriculum. From ethical reflections to database design, from corpus linguistics to quantitative autoethnography, these chapters implement and interpret the drive toward data in diverse ways.

Mathematics for Social Justice

Author : Catherine A. Buell
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Mathematics instructors are always looking for ways to engage students in meaningful and authentic tasks that utilize mathematics. At the same time, it is crucial for a democratic society to have a citizenry who can critically discriminate between “fake” and reliable news reports involving numeracy and apply numerical literacy to local and global issues. This book contains examples of topics linking math and social justice and addresses both goals. There is a broad range of mathematics used, including statistical methods, modeling, calculus, and basic algebra. The range of social issues is also diverse, including racial injustice, mass incarceration, income inequality, and environmental justice. There are lesson plans appropriate in many contexts: service-learning courses, quantitative literacy/reasoning courses, introductory courses, and classes for math majors. What makes this book unique and timely is that the most previous curricula linking math and social justice have been treated from a humanist perspective. This book is written by mathematicians, for mathematics students. Admittedly, it can be intimidating for instructors trained in quantitative methods to venture into the arena of social dilemmas. This volume provides encouragement, support, and a treasure trove of ideas to get you started. The chapters in this book were originally published as a special issue of the journal, PRIMUS: Problems, Resources, and Issues in Mathematics Undergraduate Studies.