Weapons of math destruction: how big data increases inequality and threatens democracy (2024)

английский [en], .epub, 🚀/lgli/lgrs/zlib, 0.5MB, 📘 Книга (Документальная), lgli/Z:\Bibliotik_\1\62.3.65.234\Weapons of Math Destruction - Cathy O_2527Neil_4144.epub

Weapons of math destruction: how big data increases inequality and threatens democracy 🔍

описание

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 co*cktail 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

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Альтернативное название

Weapons of math destruction : how big data increases inequality and threatens democracy

Альтернативный автор

Cathy O'Neil

Альтернативный издатель

Penguin Books, Limited

Альтернативный издатель

Penguin Uk

Альтернативный издатель

Allen Lane

Альтернативное издание

London, 2016

Альтернативное издание

UK, 2017

Альтернативное описание

Long-listed for the National Book Award 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 co*cktail 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 r?sum?s, 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. From the Hardcover edition

Альтернативное описание

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 Loan, How Much We Pay For 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. And Yet, As Cathy O'neil Reveals In This Urgent And Necessary Book, The Opposite Is True. The Models Being Used Today Are Opaque, Unregulated, And Incontestable, Even When They're Wrong. Most Troubling, They Reinforce Discrimination. 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 Cvs, Grant Or Deny Loans, Evaluate Workers, Target Voters, And Monitor Our Health. O'neil Calls On Modellers 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.

Альтернативное имя файла

lgrsnf/Z:\Bibliotik_\1\62.3.65.234\Weapons of Math Destruction - Cathy O_2527Neil_4144.epub

дата открытого доступа

2020-07-26

Weapons of math destruction: how big data increases inequality and threatens democracy (2024)

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