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2025-03-31
Weapons of Math Destruction 2025 pdf epub mobi 电子书
文 / 董小琳 我们可以将时代划分为:有大数据之前 和 有大数据之后。 为什么要这么分? 因为,谁也不能忽视,大数据对我们每个人生活方方面面的影响。 比如说: 之前,你的日子过得好不好,恐怕除了家里人,只有几个关系特别好的朋友知道。 甚至,在亲戚比较多的大家庭里,你还...
评分文 / 董小琳 我们可以将时代划分为:有大数据之前 和 有大数据之后。 为什么要这么分? 因为,谁也不能忽视,大数据对我们每个人生活方方面面的影响。 比如说: 之前,你的日子过得好不好,恐怕除了家里人,只有几个关系特别好的朋友知道。 甚至,在亲戚比较多的大家庭里,你还...
评分 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...
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.
羊烤这缠头不是早就黑过了蟆
评分太唠叨
评分太唠叨
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。
评分可能之前期待值太高 所以落差比较大.. 对fairness and accountability in ml比较陌生的人还是很推荐的。 读起来觉得大妈强项的数学模型方面可能考虑非technical读者粗略带过不过瘾, 不是专项的policy方面argument又比较sloppy...
Weapons of Math Destruction 2025 pdf epub mobi 电子书