Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy

by Cathy O'Neil

Overview

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.

Product Details

ISBN-13:	9780553418835
Publisher:	Crown Publishing Group
Publication date:	09/05/2017
Pages:	288
Sales rank:	8,913
Product dimensions:	5.10(w) x 7.90(h) x 0.70(d)

About the Author

Cathy O'Neil is a data scientist and author of the blog mathbabe.org. She earned a Ph.D. in mathematics from Harvard and taught at Barnard College before moving to the private sector, where she worked for the hedge fund D. E. Shaw. She then worked as a data scientist at various start-ups, building models that predict people’s purchases and clicks. O’Neil started the Lede Program in Data Journalism at Columbia and is the author of Doing Data Science. She is currently a columnist for Bloomberg View.

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Excerpted from "Weapons of Math Destruction"
by .
Copyright © 2017 Cathy O'Neil.
Excerpted by permission of Crown/Archetype.
All rights reserved. No part of this excerpt may be reproduced or reprinted without permission in writing from the publisher.
Excerpts are provided by Dial-A-Book Inc. solely for the personal use of visitors to this web site.

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Table of Contents

Introduction 1

Chapter 1 Bomb Parts: What Is a Model? 15

Chapter 2 Shell Shocked: My Journey of Disillusionment 32

Chapter 3 Arms Race: Going to College 50

Chapter 4 Propaganda Machine: Online Advertising 68

Chapter 5 Civilian Casualties: Justice in the Age of Big Data 84

Chapter 6 Ineligible to Serve: Getting a Job 105

Chapter 7 Sweating Bullets: On the Job 123

Chapter 8 Collateral Damage: Landing Credit 141

Chapter 9 No Safe Zone: Getting Insurance 161

Chapter 10 The Targeted Citizen: Civic Life 179

Conclusion 199

Notes 219

Index 253

Editorial Reviews

O’Neil’s book offers a frightening look at how algorithms are increasingly regulating people. . . . Her knowledge of the power and risks of mathematical models, coupled with a gift for analogy, makes her one of the most valuable observers of the continuing weaponization of big data. . . . [She] does a masterly job explaining the pervasiveness and risks of the algorithms that regulate our lives.”—The New York Times Book Review

"Weapons of Math Destruction is the Big Data story Silicon Valley proponents won't tell. . . . [It] pithily exposes flaws in how information is used to assess everything from creditworthiness to policing tactics . . . a thought-provoking read for anyone inclined to believe that data doesn't lie.”—Reuters

“This is a manual for the twenty-first century citizen, and it succeeds where other big data accounts have failed—it is accessible, refreshingly critical and feels relevant and urgent.”—Financial Times

"Insightful and disturbing."—New York Review of Books

“Weapons of Math Destruction is an urgent critique of . . . the rampant misuse of math in nearly every aspect of our lives.”—Boston Globe

“A fascinating and deeply disturbing book.”—Yuval Noah Harari, author of Sapiens

“Illuminating . . . [O’Neil] makes a convincing case that this reliance on algorithms has gone too far.”—The Atlantic

“A nuanced reminder that big data is only as good as the people wielding it.”—Wired

“If you’ve ever suspected there was something baleful about our deep trust in data, but lacked the mathematical skills to figure out exactly what it was, this is the book for you.”—Salon

“O’Neil is an ideal person to write this book. She is an academic mathematician turned Wall Street quant turned data scientist who has been involved in Occupy Wall Street and recently started an algorithmic auditing company. She is one of the strongest voices speaking out for limiting the ways we allow algorithms to influence our lives. . . . While Weapons of Math Destruction is full of hard truths and grim statistics, it is also accessible and even entertaining. O’Neil’s writing is direct and easy to read—I devoured it in an afternoon.”—Scientific American

“Indispensable . . . Despite the technical complexity of its subject, Weapons of Math Destruction lucidly guides readers through these complex modeling systems. . . . O’Neil’s book is an excellent primer on the ethical and moral risks of Big Data and an algorithmically dependent world. . . . For those curious about how Big Data can help them and their businesses, or how it has been reshaping the world around them, Weapons of Math Destruction is an essential starting place.”—National Post

“Cathy O’Neil has seen Big Data from the inside, and the picture isn’t pretty. Weapons of Math Destruction opens the curtain on algorithms that exploit people and distort the truth while posing as neutral mathematical tools. This book is wise, fierce, and desperately necessary.”—Jordan Ellenberg, University of Wisconsin-Madison, author of How Not To Be Wrong

“O’Neil has become [a whistle-blower] for the world of Big Data . . . [in] her important new book. . . .  Her work makes particularly disturbing points about how being on the wrong side of an algorithmic decision can snowball in incredibly destructive ways.”—Time

From the Publisher

[O'Neil's] knowledge of the power and risks of mathematical models, coupled with a gift for analogy, makes her one of the most valuable observers of the continuing weaponization of big data…Understanding just how big the problem is…is critical, and O'Neil does a masterly job explaining the pervasiveness and risks of the algorithms that regulate our lives.

The New York Times Book Review - Clay Shirky

06/13/2016
This taut and accessible volume, the stuff of technophobes’ nightmares, explores the myriad ways in which large-scale data modeling has made the world a less just and equal place. O’Neil speaks from a place of authority on the subject: a Barnard professor turned Wall Street quant, she renounced the latter profession after the 2008 market collapse and decided to educate laypeople. Unlike some other recent books about data collection, hers is not hysterical; she offers more of a chilly wake-up call as she walks readers through the ways the “big data” industry has facilitated social ills such as skyrocketing college tuitions, policing based on racial profiling, and high unemployment rates in vulnerable communities. She also homes in on the ways these systems are frequently destructive even to the privileged: sloppy data-gathering companies misidentify people and flag them as criminals, and algorithms determine employee value during company-wide firings. The final chapter, in which O’Neil discusses Facebook’s increasing electoral influence, feels eerily prescient. She offers no one easy solution, but has several reasonable suggestions as to how the future can be made more equitable and transparent for all. Agent: Jay Mandel, William Morris Endeavor. (Sept.)

Publishers Weekly

11/15/2016
As mathematical models affect more and more aspects of our lives, it is crucial to understand that algorithms are not neutral, free from human prejudice and fallibility; instead, those biases and failings are encoded into the systems. Data scientist O'Neil, who blogs at mathbabe.org, explores this premise in depth and chillingly describes the extent to which data-driven, algorithm-based decision making in such areas as hiring, policing, lending, education, and health care actually increases inequality. With barely contained exasperation, O'Neil chronicles the way these "weapons of math destruction"—opaque and unregulated—shape all lives, and, especially, those of the poor. More than just sounding the clarion call to action, O'Neil seeks to empower her readers to ask questions about the algorithms and to demand change. Though the subject matter is alarming and dire, O'Neil's dry wit and ease when describing complicated ideas is more enlivening than depressing. VERDICT This important book will be eye-opening to many readers, possibly even those involved with the kind of models O'Neil criticizes.—Rachel Bridgewater, Portland Community Coll. Lib., OR

Library Journal

★ 2016-07-19
How ill-conceived algorithms now micromanage America’s economy, from advertising to prisons.“Welcome to the dark side of Big Data,” writes math guru O’Neil (Doing Data Science: Straight Talk from the Frontline, 2013, etc.), a blogger (mathbabe.org) and former quantitative analyst at the hedge fund D.E. Shaw. In this simultaneously illuminating and disturbing account, she describes the many ways in which widely used mathematic models—based on “prejudice, misunderstanding, and bias”—tend to punish the poor and reward the rich. The most harmful such models, which she calls “Weapons of Math Destruction,” often have devastating effects on people when they are going to college, borrowing money, getting sentenced to prison, or finding and holding a job. For example: credit scores are used to evaluate potential hires (assuming bad scores correlate with bad job performance, which is often not true); for-profit colleges use data to target and prey on vulnerable strivers, often plunging them into debt; auto insurance companies judge applicants by their consumer patterns rather than their driving records; crime predictive software often leads police to focus on nuisance crimes in impoverished neighborhoods. As the author notes, the harmful effects are apparent “when a poor minority teenager gets stopped, roughed up, and put on warning by the local police, or when a gas station attendant who lives in a poor zip code gets hit with a higher insurance bill.” She notes the same mathematical models “place the comfortable classes of society in their own marketing silos,” jetting them off to vacations in Aruba, wait-listing them at Wharton, and generally making their lives “smarter and easier.” The author writes with passion—a few years ago she became disillusioned over her hedge fund modeling and joined the Occupy movement—but with the authority of a former Barnard professor who is outraged at the increasingly wrongheaded use of mathematics. She convincingly argues for both more responsible modeling and federal regulation. An unusually lucid and readable look at the daunting algorithms that govern so many aspects of our lives.

Kirkus Reviews

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