Weapons of math destruction
Weapons of math destruction
复制标题
数学毁灭性武器
DOI：
10.1080/23299460.2018.1495027
复制
发表时间：
2018
影响因子：
3.9
通讯作者：
Thomas S. Woodson
中科院分区：
文献类型：
--
作者：
Thomas S. Woodson
算法和数据模型几乎渗透到我们生活的每一个领域。搜索引擎根据我们的在线历史记录建立的信息定制他们的广告。杂货店会跟踪我们的购买行为，并开发算法来用优惠券诱惑我们。数据模型决定了我们是否被雇用、解雇和/或晋升。从表面上看，这些模型似乎是有效和公平的，但正如Cathy O 'Neil在《数学毁灭性武器》中所强调的那样，它们远非价值中立的数学方程。相反，它们嵌入了数百个假设，往往是有缺陷的，并可能得出错误的结论，对社会造成破坏性影响。更糟糕的是，数学毁灭性武器（WMD）并不会平等地伤害所有人：贫困、边缘化和脆弱的社区遭受大规模毁灭性武器负面后果的风险更高，因为数据模型设计者没有考虑他们的利益，因为他们对抗负面后果的力量更小。O 'Neil是一位大数据分析师，他正在揭露这个领域混乱的底层。在写这本书之前，奥尼尔获得了博士学位。他在数学领域取得了巨大的成就，并花了数年时间为金融公司和互联网广告公司构建模型。在创建这些模型的过程中，她看到了分析工具的许多缺点，以及用于构建模型的技术如何不完整，草率和歧视。在数学毁灭性武器的开头，奥尼尔承认她不是大数据模型的布道者，在整本书中，我们了解到她是严格限制大规模杀伤性武器实施的支持者。事实上，奥尼尔指出，“以公平的名义，一些数据应该保持不被处理”（150）。很少有数据科学家提出让数据不被处理，这本书是对大数据模型乐观浪潮的一个惊人的平衡。
Algorithm and data models are penetrating almost every area of our lives. Search engines tailor their ads based on information established by our online history. Grocery stores track our purchases and develop algorithms to tempt us with coupons. Data models determine whether we are hired, fired, and/or promoted. On the surface, these models may appear to be efficient and fair, but as Cathy O’Neil forcibly argues in Weapons of Math Destruction, they can be far from value-neutral math equations. Rather, they are embedded with hundreds of assumptions, are often flawed, and can draw faulty conclusions with devastating effects for society. To make matters worse, weapons of math destruction (WMDs) do not harm all individuals equally: poor, marginalized, and vulnerable communities are at a higher risk of suffering negative consequences from WMDs because data model designers do not consider their interests and because they have less power to fight against negative outcomes. O’Neil is a big data analyst who is exposing the messy underbelly of the field. Before writing this book, O’Neil earned a Ph. D. in Mathematics and spent years building models for finance companies and internet advertising firms. While creating these models, she saw many of the shortcomings of the analytical tools and how the techniques used to build the models can be incomplete, sloppy, and discriminatory.At the beginning of Weapons of Math Destruction, O’Neil confesses that she is not an evangelist of big data models and throughout the book we learn that she is a proponent of severely curtailing the implementation of WMDs. In fact, O’Neil states that ‘in the name of fairness, some of the data should remain uncrunched’(150). It is rare for a data scientist to propose leaving data un-crunched and this book is a striking counterbalance to the wave of optimism over big data models.