原文書：Weapons of Math Destruction (數(shù)學(xué)殺傷性武器)

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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.

我們生活在算法的時(shí)代。越來越多的影響我們生活的決定由數(shù)學(xué)模型而不是人類做出，例如我們上學(xué)的地方，我們能否獲得汽車貸款，以及我們?yōu)榻】当ｋU(xiǎn)支付多少。從理論上講，這應(yīng)該導(dǎo)致更大的公平：每個(gè)人都是按照相同的規(guī)則來判斷的，偏見被消除了。

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.

但正如凱西·奧尼爾（Cathy O'Neil）在這本緊急且必要的書中所揭示的那樣，事實(shí)恰恰相反�，F(xiàn)在所使用的模型是不透明、不受管制，且不可測(cè)試的，甚至在它們錯(cuò)誤的情況下。最令人不安的是，他們強(qiáng)化了歧視：如果一個(gè)貧困學(xué)生被貸款模型認(rèn)定風(fēng)險(xiǎn)過高（憑借他的郵政編碼）而無法獲得貸款，那么他就會(huì)脫離能夠使他擺脫貧困的教育，并出現(xiàn)惡性循環(huán)。模型通過鼓勵(lì)幸運(yùn)的人和懲罰受壓迫的人，創(chuàng)造了一個(gè)“民主雞尾酒”。歡迎來到大數(shù)據(jù)的黑暗面。

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.

追溯一個(gè)人生命的曲線，奧尼爾揭示了塑造個(gè)人和社會(huì)未來的黑箱模型。這些“數(shù)學(xué)殺傷性武器”用來為教師和學(xué)生評(píng)分，給簡(jiǎn)歷排序，決定是否發(fā)放貸款，評(píng)估工作人員，確定目標(biāo)選民，設(shè)置假釋條件，并監(jiān)測(cè)我們的健康。

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.

奧尼爾呼吁建模者為他們的算法承擔(dān)更多的責(zé)任，決策者也需要更加負(fù)責(zé)地規(guī)范這些算法的使用。但最終，我們要對(duì)管理我們生活的模型更加了解。這本重要的書使我們能夠提出棘手的問題，進(jìn)而揭開真相，要求改變。
Review:
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

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關(guān)鍵詞：Destruction Weapons struct Math Mat

我的天這可是一本好書啊，為什么沒人點(diǎn)贊！！給樓主一個(gè)大大的贊！！另外求Virginia Eubanks的 Automating Inequality這本書，18年1月才出版的，不知道國(guó)內(nèi)有沒有小伙伴買了kindle版

這是本好書啊，中信剛剛翻譯出來了，怎么沒人看

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xeixei！

好貼就點(diǎn)贊，一起拿積分

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