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2024-05-10
Weapons of Math Destruction 2024 pdf epub mobi 电子书
【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 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...
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.
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。
评分更像是essay合集。道理都还是中肯的。读起来也很快。个人口味问题,更喜欢不论长短薄厚,都能一板一眼扎实严谨的论述结构,而不过多依靠于类似类比。
评分Big data ethics, 数据和模型导致了社会资源的重新配置很有启发。抒情和道德抨击减一星。
评分羊烤这缠头不是早就黑过了蟆
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。
Weapons of Math Destruction 2024 pdf epub mobi 电子书