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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.
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.
Due to publisher restrictions the library cannot purchase additional copies of this title, and we apologize if there is a long waiting list. Be sure to check for other copies, because there may be other editions available.
Due to publisher restrictions the library cannot purchase additional copies of this title, and we apologize if there is a long waiting list. Be sure to check for other copies, because there may be other editions available.
Excerpts-
From the book
1
BOMB PARTS What Is a Model?
It was a hot August afternoon in 1946. Lou Boudreau, the player-manager of the Cleveland Indians, was having a miserable day. In the first game of a doubleheader, Ted Williams had almost single-handedly annihilated his team. Williams, perhaps the game’s greatest hitter at the time, had smashed three home runs and driven home eight. The Indians ended up losing 11 to 10.
Boudreau had to take action. So when Williams came up for the first time in the second game, players on the Indians’ side started moving around. Boudreau, the shortstop, jogged over to where the second baseman would usually stand, and the second baseman backed into short right field. The third baseman moved to his left, into the shortstop’s hole. It was clear that Boudreau, perhaps out of desperation, was shifting the entire orientation of his defense in an attempt to turn Ted Williams’s hits into outs.
In other words, he was thinking like a data scientist. He had analyzed crude data, most of it observational: Ted Williams usually hit the ball to right field. Then he adjusted. And it worked. Fielders caught more of Williams’s blistering line drives than before (though they could do nothing about the home runs sailing over their heads).
If you go to a major league baseball game today, you’ll see that defenses now treat nearly every player like Ted Williams. While Boudreau merely observed where Williams usually hit the ball, managers now know precisely where every player has hit every ball over the last week, over the last month, throughout his career, against left-handers, when he has two strikes, and so on. Using this historical data, they analyze their current situation and calculate the positioning that is associated with the highest probability of success. And that sometimes involves moving players far across the field.
Shifting defenses is only one piece of a much larger question: What steps can baseball teams take to maximize the probability that they’ll win? In their hunt for answers, baseball statisticians have scrutinized every variable they can quantify and attached it to a value. How much more is a double worth than a single? When, if ever, is it worth it to bunt a runner from first to second base?
The answers to all of these questions are blended and combined into mathematical models of their sport. These are parallel universes of the baseball world, each a complex tapestry of probabilities. They include every measurable relationship among every one of the sport’s components, from walks to home runs to the players themselves. The purpose of the model is to run different scenarios at every juncture, looking for the optimal combinations. If the Yankees bring in a right-handed pitcher to face Angels slugger Mike Trout, as compared to leaving in the current pitcher, how much more likely are they to get him out? And how will that affect their overall odds of winning?
Baseball is an ideal home for predictive mathematical modeling. As Michael Lewis wrote in his 2003 bestseller, Moneyball, the sport has attracted data nerds throughout its history. In decades past, fans would pore over the stats on the back of baseball cards, analyzing Carl Yastrzemski’s home run patterns or comparing Roger Clemens’s and Dwight Gooden’s strikeout totals. But starting in the 1980s, serious statisticians started to investigate what these figures, along with an avalanche of new ones, really meant: how they translated into wins, and how executives could maximize success with a minimum of dollars.
“Moneyball” is now shorthand for any...
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.
Reviews-
June 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.
November 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
Copyright 2016 Library Journal, LLC Used with permission.
New York Times Book Review
"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."
Reuters
"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."
Scientific American
"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."
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