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.
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.
The answer is yes. A model, after all, is nothing more than an abstract representation of some process, be it a baseball game, an oil company’s supply chain, a foreign government’s actions, or a movie theater’s attendance. Whether it’s running in a comp...
评分The answer is yes. A model, after all, is nothing more than an abstract representation of some process, be it a baseball game, an oil company’s supply chain, a foreign government’s actions, or a movie theater’s attendance. Whether it’s running in a comp...
评分感谢 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...
评分【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 ...
评分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...
这本中文版已经引进了。作者懂技术,更看得懂技术所带来社会动力,乃至一些技术无法预见的后果……当然视角是左翼的
评分通篇读完觉得稍空了一些 中途回想起实习时的贷款延期批准模型 误判率数字背后都联系着顾客生计 唉想来不止是一个技术问题这么简单 作者自己从业经历背景也蛮厉害的 总体论调不反智!
评分名字起得不错,作者对“数学杀伤性武器“的定义也很明确:opaque, large scale ,disruptive. 现实生活中的例子也有清晰阐述,包括 value added model 并不能真正反映教师的水平(很多差生+很多好生的班级能够进步的空间不大,相反比较中等的班级更容易通过提高成绩而增加教师的评分);大数据分析信贷对弱势群体的不公;自动调班系统让零售业打工者疲于奔命等。
评分想知道"大数据"毛病的不用读了。完全是一个"science is bad because it hurts my feeling"的完美案例。这下某些低等物种又可以造反有理了。
评分大数据模型在参数选择上的任意,数据统计上的不科学,模型适用的不科学推广,导致大数据模型在招生就业犯罪和选举问题上的不公正和不平等。虽然都是举例,但介绍了数据对人生活加以掌控的方方面面。
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