Weapons of Math Destruction
How Big Data Increases Inequality and Threatens Democracy. Ausgezeichnet: Euler Book Prize
Buch (Taschenbuch, Englisch)
Buch (Taschenbuch, Englisch)
inkl. gesetzl. MwSt.zzgl. VersandkostenVersandfertig innert 1 - 2 Werktagen
- Kostenlose Lieferung ab Fr. 30 i
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
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."
"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."
"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."
"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."
"A nuanced reminder that big data is only as good as the people wielding it."
"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."
"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."
"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."
"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