Review by Publisher's Weekly Review
This taut and accessible volume, the stuff of technophobes' nightmares, explores the myriad ways in which large-scale data modeling has made the world a less just and equal place. O'Neil speaks from a place of authority on the subject: a Barnard professor turned Wall Street quant, she renounced the latter profession after the 2008 market collapse and decided to educate laypeople. Unlike some other recent books about data collection, hers is not hysterical; she offers more of a chilly wake-up call as she walks readers through the ways the "big data" industry has facilitated social ills such as skyrocketing college tuitions, policing based on racial profiling, and high unemployment rates in vulnerable communities. She also homes in on the ways these systems are frequently destructive even to the privileged: sloppy data-gathering companies misidentify people and flag them as criminals, and algorithms determine employee value during company-wide firings. The final chapter, in which O'Neil discusses Facebook's increasing electoral influence, feels eerily prescient. She offers no one easy solution, but has several reasonable suggestions as to how the future can be made more equitable and transparent for all. Agent: Jay Mandel, William Morris Endeavor. (Sept.) © Copyright PWxyz, LLC. All rights reserved.
(c) Copyright PWxyz, LLC. All rights reserved
Review by Kirkus Book Review
How ill-conceived algorithms now micromanage Americas economy, from advertising to prisons.Welcome to the dark side of Big Data, writes math guru ONeil (Doing Data Science: Straight Talk from the Frontline, 2013, etc.), a blogger (mathbabe.org) and former quantitative analyst at the hedge fund D.E. Shaw. In this simultaneously illuminating and disturbing account, she describes the many ways in which widely used mathematic modelsbased on prejudice, misunderstanding, and biastend to punish the poor and reward the rich. The most harmful such models, which she calls Weapons of Math Destruction, often have devastating effects on people when they are going to college, borrowing money, getting sentenced to prison, or finding and holding a job. For example: credit scores are used to evaluate potential hires (assuming bad scores correlate with bad job performance, which is often not true); for-profit colleges use data to target and prey on vulnerable strivers, often plunging them into debt; auto insurance companies judge applicants by their consumer patterns rather than their driving records; crime predictive software often leads police to focus on nuisance crimes in impoverished neighborhoods. As the author notes, the harmful effects are apparent when a poor minority teenager gets stopped, roughed up, and put on warning by the local police, or when a gas station attendant who lives in a poor zip code gets hit with a higher insurance bill. She notes the same mathematical models place the comfortable classes of society in their own marketing silos, jetting them off to vacations in Aruba, wait-listing them at Wharton, and generally making their lives smarter and easier. The author writes with passiona few years ago she became disillusioned over her hedge fund modeling and joined the Occupy movementbut with the authority of a former Barnard professor who is outraged at the increasingly wrongheaded use of mathematics. She convincingly argues for both more responsible modeling and federal regulation. An unusually lucid and readable look at the daunting algorithms that govern so many aspects of our lives. Copyright Kirkus Reviews, used with permission.
Copyright (c) Kirkus Reviews, used with permission.
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