Weapons of Math Destruction

书名：Weapons of Math DestructionHowBigDataIncreasesInequalityandThreatensDemocracy
作者：CathyO'Neil
译者：
ISBN：9780553418811
出版社：Crown
出版时间：2016-9-6
格式：epub/mobi/azw3/pdf
页数：272
豆瓣评分： 7.7

书籍简介：

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：

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.

书友短评：

@ Prunus d 比刚读的那本Automating Inequality不知道高到哪里去了。WMD灵魂三问：Opacity: Is it opaque? Damage: Is it unfair? (Does it work against the subject's interest? *pernicious feedback loops) Scale: Can it scale? 以及proxy的滥用是很多杯具算法的源头。 @ newlight 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！！ @ wang [有声书] 虽然会被吐槽太左太口水，我觉得写得还是很好的！虽然是本行，但模型大量运用在商业啊竞选啊等等领域的具体情况和问题，我了解不多，这本书提供了一个好的窗口。很喜欢作者对model浅显易懂的解读，尤其强调了scale up前后的对比；也很引发关于模型对群体和对个人的影响的思考：模型用的变量是群体属性，模型作用在个人身上必然问题重重，这个点也是以后教课值得讨论的。这本书很适合做课外reading，尤其是关于现在很火的data ethics。另Cynthia Dwork似乎出现在了结尾章节，真是涉猎广泛呢！又另有声书是作者自己读的哈～ @ Walter Prof. Moody: Constant vigilance! 以一个热心大妈的絮絮叨叨告诉人在哪些方面，为什么，以及如何提高警惕。也许只是换一个方式躺平。

本书所获赞誉
前言
第一章 盲点炸弹 不透明、规模化和毁灭性
第二章 操纵与恐吓 弹震症患者的醒悟
第三章 恶意循环 排名模型的特权与焦虑
第四章 数据经济 掠夺式广告的赢家
第五章 效率权衡与逻辑漏洞 大数据时代的正义
第六章 筛选 颅相学的偏见强化
第七章 反馈 辛普森悖论的噪声
第八章 替代变量和间接损害 信用数据的陷阱
第九章 “一般人”公式 沉溺与歧视
第十章 正面的力量 微目标的出发点
结论
致谢
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