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Weapons of Math Destruction

How Big Data Increases Inequality and Threatens Democracy. Ausgezeichnet: Euler Book Prize

Longlisted for the National Book Award
New York Times Bestseller

A former Wall Street quant sounds an alarm on the mathematical models that pervade modern life - and threaten to rip apart our social fabric

We live in the age of the algorithm. Increasingly, the decisions that affect our lives-where we go to school, whether we get a car loan, how much we pay for health insurance-are being made not by humans, but by mathematical models. In theory, this should lead to greater fairness: Everyone is judged according to the same rules, and bias is eliminated.

But as Cathy O'Neil reveals in this urgent and necessary book, the opposite is true. The models being used today are opaque, unregulated, and uncontestable, even when they're wrong. Most troubling, they reinforce discrimination: If a poor student can't get a loan because a lending model deems him too risky (by virtue of his zip code), he's then cut off from the kind of education that could pull him out of poverty, and a vicious spiral ensues. Models are propping up the lucky and punishing the downtrodden, creating a "toxic cocktail for democracy." Welcome to the dark side of Big Data.

Tracing the arc of a person's life, O'Neil exposes the black box models that shape our future, both as individuals and as a society. These "weapons of math destruction" score teachers and students, sort résumés, grant (or deny) loans, evaluate workers, target voters, set parole, and monitor our health.

O'Neil calls on modelers to take more responsibility for their algorithms and on policy makers to regulate their use. But in the end, it's up to us to become more savvy about the models that govern our lives. This important book empowers us to ask the tough questions, uncover the truth, and demand change.

- Longlist for National Book Award (Non-Fiction)
- Goodreads, semi-finalist for the 2016 Goodreads Choice Awards (Science and Technology)
- Kirkus, Best Books of 2016
- New York Times, 100 Notable Books of 2016 (Non-Fiction)
- The Guardian, Best Books of 2016
- WBUR's "On Point," Best Books of 2016: Staff Picks
- Boston Globe, Best Books of 2016, Non-Fiction
Rezension
A New York Times Book Review Notable Book of 2016
A Boston Globe Best Book of 2016
One of Wired's Required Reading Picks of 2016
One of Fortune's Favorite Books of 2016
A Kirkus Reviews Best Book of 2016
A Chicago Public Library Best Book of 2016
A Nature.com Best Book of 2016
An On Point Best Book of 2016
New York Times Editor's Choice
A Maclean's Bestseller
Winner of the 2016 SLA-NY PrivCo Spotlight Award

"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."
-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 21st-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; The Guardian's Best Books of 2016

"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

"Readable and engaging... succinct and cogent... Weapons of Math Destruction is The Jungle of our age... [It] should be required reading for all data scientists and for any organizational decision-maker convinced that a mathematical model can replace human judgment."
-Mark Van Hollebeke, Data and Society: Points

"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 algori
Portrait
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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    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 statistical approach in domains long ruled by the gut. But baseball represents a healthy case study-and it serves as a useful contrast to the toxic models, or WMDs, that are popping up in so many areas of our lives. Baseball models are fair, in part, because they're transparent. Everyone has access to the stats and can understand more or less how
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Beschreibung

Produktdetails

Einband Taschenbuch
Seitenzahl 288
Erscheinungsdatum 05.09.2017
Sprache Englisch
ISBN 978-0-553-41883-5
Verlag Random House LCC US
Maße (L/B/H) 20.2/13.1/2 cm
Gewicht 220 g
Verkaufsrang 4171
Buch (Taschenbuch, Englisch)
Buch (Taschenbuch, Englisch)
Fr. 18.90
Fr. 18.90
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