Weapons of Math Destruction
How Big Data Increases Inequality and Threatens Democracy. Ausgezeichnet: Euler Book Prize
(Sprache: Englisch)
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...
"A manual for the twenty-first-century citizen . . . relevant and...
lieferbar
versandkostenfrei
Buch (Kartoniert)
14.50 €
Produktdetails
Produktinformationen zu „Weapons of Math Destruction “
Klappentext zu „Weapons of Math Destruction “
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 • 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.
Lese-Probe zu „Weapons of Math Destruction “
1BOMB 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
... mehr
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,
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,
... weniger
Autoren-Porträt von Cathy O'Neil
Cathy O'Neil
Bibliographische Angaben
- Autor: Cathy O'Neil
- 2017, 288 Seiten, Maße: 13,1 x 20,2 cm, Kartoniert (TB), Englisch
- Verlag: Crown
- ISBN-10: 0553418831
- ISBN-13: 9780553418835
- Erscheinungsdatum: 24.08.2017
Sprache:
Englisch
Pressezitat
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
... mehr
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
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
... weniger
Kommentar zu "Weapons of Math Destruction"
0 Gebrauchte Artikel zu „Weapons of Math Destruction“
Zustand | Preis | Porto | Zahlung | Verkäufer | Rating |
---|
Schreiben Sie einen Kommentar zu "Weapons of Math Destruction".
Kommentar verfassen