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

O'Neil, Cathy

ISBN 10: 0553418831 / ISBN 13: 9780553418835

Published by Crown, 2017

About this Item

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Bibliographic Details

• Title: Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy
• Publisher: Crown
• Publication Date: 2017
• Language: English
• ISBN 10: 0553418831
• ISBN 13: 9780553418835
• Binding: Soft cover
• Condition: like_new

About this title

Synopsis

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.

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.