Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy
| Author | : | |
| Rating | : | 4.17 (649 Votes) |
| Asin | : | 0553418815 |
| Format Type | : | paperback |
| Number of Pages | : | 272 Pages |
| Publish Date | : | 2015-05-03 |
| Language | : | English |
DESCRIPTION:
She makes a compelling case that math is being used to squeeze marginalized segments of society and magnify inequities. As the empire of big data continues to expand, Cathy O’Neil’s reminder of the need for vigilance is welcome and necessary.”—American Prospect“An avowed math nerd, O’Neil has written an engaging description of the effect of crunched data on our lives.”—Hicklebee’s, San Francisco Chronicle“By tracking how algorithms shape people's lives at every stage, O'Neil makes a compelling case that our bot overlords are using data to discriminate unfairly and foreclose democratic choices. 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. Weapons of Math De
E. in mathematics from Harvard and taught at Barnard College before moving to the private sector, where she worked for the hedge fund D. She earned a Ph.D. 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. Cathy O'Neil is a data scientist and author of the blog mathbabe. She then worked as a data scientist at various start-ups, building models that predict peop
"Stop Using Math as a Weapon" according to Amazon Customer. So here you are on Amazon's web page, reading about Cathy O'Neil's new book, Weapons of Math Destruction. Amazon hopes you buy the book (and so do I, it's great!). But Amazon also hopes it can sell you some other books while you're here. That's why, in a prominent place on the page, you see a section entitled:Customers Who Bought This Item Also BoughtThis section is Amazon's way of using what it knows -- which book you're looking at, and sales data collected across all its customers -- to recommend other books that you might be interested in. It's a very simple, and succes. "They back up their analysis with reams of statistics, which give them the studied air of evenhanded science." CodeMaster Talon I struggled with the star rating for this book. There are certainly aspects of the work that merit five stars. And it is VERY thought-provoking, like a good book should be. But there are flaws, significant ones, with the biggest flaw being a glaring over-simplification of many of the systems that O'Neil decries in the book. I don't know if O'Neil has personally ever had to take a psychology test to get a job, worked under the Kronos scheduling system, lived in a neighborhood with increased police presence due to crime rates, been victimized by insurance rates adjusted to z. WMD offered insights into some of the threats posed by Big Data The book looks at the black box algorithms and their misuses. It starts strong, but becomes a repeat of the same story line in late chapters. It definitely gave background of the dangers of Big Data in a number of industries, and painted the grim picture of how this is impacting society today.I am a high school statistics teacher and this afforded me the opportunity to engage my students in discussions of ethics related to many situations found in this book.It was an enjoyable, if disturbing, read.
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. 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. Longlisted for the National Book Award New York Times BestsellerA former Wall Street quant sounds an alarm on the mathematical models that perv