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 mathema...
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.
作者简介
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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.
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.
とりさがし
格列柯南
中信出版社2018版
左其盛
中信出版社2018版
ZG2014 NC Xue
中信出版社2018版
catteforry
中信出版社2018版
0 有用 newlight 2017-01-09 07:07:07
迷信大数据的时代，需要好好读一下这本书
5 有用 羊肉烤黄皮子 2017-09-10 16:06:58
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。
0 有用 Prunus d 2018-10-19 11:29:43
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
3 有用 clover 2016-09-13 11:37:45
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。
0 有用 Miss Coconut 2018-10-02 13:33:34
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... 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！！ (展开)
0 有用 Mamula 2023-07-29 02:05:41 美国
看到后头好无聊
0 有用 你来人间一趟 2023-06-15 03:21:51 美国
[Libby] 我们不应该假借模型的客观性的面纱来忽视其蕴含的偏见以及推脱其应尽的社会责任，但最终的解决方案只能是更好的模型：更好的input, 更好的feature selection, 更好的把equity明确写入了的objective function……
0 有用 镇守府的尼克桑 2023-04-30 12:19:07 美国
看网飞的the social dilemma 的时候注意到的Cathy O'Neil，今天终于全本看完。总的来说有些浅显了，罗列问题没有解决方案。这个时代，好算法和破坏性的算法边界也非常模糊，不但取决于用了什么技术和feature，也取决于人们怎么应用它。看到结尾还是很认同，这不是free market可以自我解决的事情，somehow和90年代时一样，我们需要一次伦理和立法上的revolutio... 看网飞的the social dilemma 的时候注意到的Cathy O'Neil，今天终于全本看完。总的来说有些浅显了，罗列问题没有解决方案。这个时代，好算法和破坏性的算法边界也非常模糊，不但取决于用了什么技术和feature，也取决于人们怎么应用它。看到结尾还是很认同，这不是free market可以自我解决的事情，somehow和90年代时一样，我们需要一次伦理和立法上的revolution。 (展开)
0 有用 游栖云杪 2023-04-19 09:34:44 美国
Chapter2讨伐finance quant的部分真是再合适不过的入职前教育哈哈哈哈哈哈哈。
0 有用 lil 2023-01-29 14:25:54 美国
科技也没那么坏，但是需要更多有这方面思考的人