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2024-12-22
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【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 评分图书标签: 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.
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
评分学术界的人或许会说这里都是例子,比较浅薄,不成体系也没有深度。但我觉得这里的讨论都非常有价值,作者也非常真诚。作为一个比较早的讨论统计和数据方法的伦理以及社会公平的读物来说,我觉得值得赞美一下。
评分迷信大数据的时代,需要好好读一下这本书
评分直译过来,就是数杀器。对大数据的合理使用,可以提高效率;否则,加剧社会不公。
评分大数据伦理讨论小合集。身在tech公司做大数据的东西,经常考虑这方面的东西。模型再好也难以100%正确,而那很小的一部分却的确能影响他们的生活。赞同作者的一些批评,但是并不能因噎废食。研究者更应该努力把模型做得更好(大部分批评都焦聚在feature selection不对,model不对之类的方面),因为相比起来,alternative更加不可取---信息太少纯粹靠拍脑袋做决定。另外,这名字起得太好了!!
Weapons of Math Destruction 2024 pdf epub mobi 电子书