Weapons of Math Destruction

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Catherine ("Cathy") Helen O'Neil is an American mathematician and the author of the blog mathbabe.org and several books on data science, including Weapons of Math Destruction. She was the former Director of the Lede Program in Data Practices at Columbia University Graduate School of Journalism, Tow Center and was employed as Data Science Consultant at Johnson Research Labs.

She lives in New York City and is active in the Occupy movement.

出版者:Crown
作者:Cathy O'Neil
出品人:
页数:272
译者:
出版时间:2016-9-6
价格:USD 26.00
装帧:Hardcover
isbn号码:9780553418811
丛书系列:
图书标签:
  • 大数据 
  • 社会学 
  • 美国 
  • 数字社会学 
  • inequality 
  • 数学 
  • 社会 
  • 政治科学 
  •  
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A former Wall Street quant sounds an alarm on mathematical modeling—a pervasive new force in society that threatens to undermine democracy and widen inequality.

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 shocking 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 race or neighborhood), 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, from college to retirement, O’Neil exposes the black box models that shape our future, both as individuals and as a society. Models that score teachers and students, sort resumes, grant (or deny) loans, evaluate workers, target voters, set parole, and monitor our health—all have pernicious feedback loops. They don’t simply describe reality, as proponents claim, they change reality, by expanding or limiting the opportunities people have. O’Neil calls on modelers to take more responsibility for how their algorithms are being used. 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.

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【春上春树随喜文化】 算法是层级和并行思维的融合 可视化,标准化,规模化,全球化 去中心化,分布式计算,智能虚拟助手 乃至宗教般毋庸置疑的 民主和科学的感召 最后所有人被既得利益者 网罗为囊中之物 辛普森悖论 是《国富论》所谓的 看不见的手 阶层难以穿透 跃迁机会渺茫 ...

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大数据是近年来特别火热的词,不管是不是互联网企业,都随时往大数据身上靠,仿佛一下子能提高自己逼格一样。在这种火热的气氛中,很多人往往对于大数据能做什么,做的好事多还是坏事多,不去反思和检讨,也很少有人愿意去听别人的反思。 音乐平台总监们的失算 记得《中国新说...  

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【春上春树随喜文化】 算法是层级和并行思维的融合 可视化,标准化,规模化,全球化 去中心化,分布式计算,智能虚拟助手 乃至宗教般毋庸置疑的 民主和科学的感召 最后所有人被既得利益者 网罗为囊中之物 辛普森悖论 是《国富论》所谓的 看不见的手 阶层难以穿透 跃迁机会渺茫 ...

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感谢 recall 这本书的不知名同学,谢谢你逼得我用4个小时读完。 作者创造了“数学杀伤性武器”(Weapons of Math Destruction, WMD)这个词指代统计模型,探讨现实生活中统计模型的大规模应用对社会的影响。 正面例子是棒球、篮球比赛的分析,可以即时调整战术(参考《点球成金...  

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作者在华尔街对冲基金德绍集团担任过金融工程师,后来去银行做过风险分析,再后来去做旅游网站的用户分析。后来辞职专门揭露美国社会生活背后的各种算法的阴暗面。 书中提到的算法的技术缺陷,我归纳为两点:第一个比较致命:不准确。不准确有两种体现,首先是算法先天的问题,...  

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观点有意思,但是这样就写出书了。感觉就是博文综合。

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观点有意思,但是这样就写出书了。感觉就是博文综合。

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太唠叨

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羊烤这缠头不是早就黑过了蟆

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认真推荐给每一位朋友。大多数人并没有意识到借着大数据(或人工智能、算法等)的旗号,科技巨头和政府的作恶能力有多可怕。特别是国内,针对这方面在制度和舆论上的制衡更是几乎为零。想想这一年多来各种新闻吧,比如某某积分系统,比如 7 分钟识别定位,我们更应感到毛骨悚然

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