Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy
Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy
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数学毁灭性武器：大数据如何加剧不平等并威胁民主
DOI：
10.1177/0256090919853933
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发表时间：
2016
期刊：
影响因子：
--
通讯作者：
Shikha Verma
中科院分区：
文献类型：
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作者：
Shikha Verma
一位前华尔街定量分析师对现代生活中普遍存在的数学模型发出了警告，这些模型有可能撕裂我们的社会结构。越来越多地，影响我们生活的决定我们去哪里上学，我们是否得到汽车贷款，我们支付多少医疗保险不是由人类，而是由数学模型。从理论上讲，这应该会带来更大的公平性：每个人都根据相同的规则进行评判，偏见被消除。但正如凯茜·奥尼尔在这本紧迫而必要的书中所揭示的那样，事实恰恰相反。今天使用的模型是不透明的，不受监管的，不稳定的，即使它们是错误的。最令人不安的是，它们强化了歧视：如果一个贫困学生因为贷款模式认为他风险太大（由于他的邮政编码）而无法获得贷款，那么他就被切断了可以让他摆脱贫困的教育，并陷入恶性循环。模型正在支持幸运者，惩罚受压迫者，为民主制造有毒的鸡尾酒。欢迎来到大数据的黑暗面。追踪一个人的生活轨迹，ONEIL揭示了塑造我们未来的黑箱模型，无论是作为个人还是作为一个社会。这些数学毁灭性的武器给老师和学生打分，给简历排序，发放（或拒绝）贷款，评估工人，瞄准选民，设定假释，监控我们的健康。ONeil呼吁建模者对他们的算法承担更多责任，并呼吁政策制定者规范其使用。但最终，这取决于我们对管理我们生活的模式更加了解。这本重要的书使我们能够提出坚韧的问题，揭示真相，并要求改变。
A former Wall Street quant sounds an alarm on the mathematical models that pervade modern life and threaten to rip apart our social fabricWe live in the age of the algorithm. Increasingly, the decisions that affect our liveswhere we go to school, whether we get a car loan, how much we pay for health insuranceare 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 ONeil reveals in this urgent and necessary book, the opposite is true. The models being used today are opaque, unregulated, and uncontestable, even when theyre wrong. Most troubling, they reinforce discrimination: If a poor student cant get a loan because a lending model deems him too risky (by virtue of his zip code), hes 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 persons life, ONeil exposes the black box models that shape our future, both as individuals and as a society. These weapons of math destruction score teachers and students, sort rsums, grant (or deny) loans, evaluate workers, target voters, set parole, and monitor our health. ONeil calls on modelers to take more responsibility for their algorithms and on policy makers to regulate their use. But in the end, its 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.