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.
发表于2024-11-02
Weapons of Math Destruction 2024 pdf epub mobi 电子书
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...
评分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,...
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
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.
观点有意思,但是这样就写出书了。感觉就是博文综合。
评分一篇讨伐大数据的檄文。与那些赞歌不同,作者解释各行各业中所用的数学模型(以及人们应对这些模型的方法)背后所蕴藏的种种歧视、黑箱与不公。这些阴暗面加剧了当今社会的贫富差距和底层人民的愤怒,监管时不我待。
评分迷信大数据的时代,需要好好读一下这本书
评分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https://book.douban.com/review/9331833/
Weapons of Math Destruction 2024 pdf epub mobi 电子书