Chapters 9–10 and Conclusion Summary

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Last Reviewed on April 2, 2020, by eNotes Editorial. Word Count: 1301

Chapter 9: No Safe Zone: Getting Insurance

In 1896, Frederick Hoffman, a statistician who worked for the Prudential Insurance Company, produced a report which argued that the lives of black Americans were so precarious as to be uninsurable. This analysis was a WMD, containing statistical errors and confusing correlation with causation. The insurance industry, however, accepted Hoffman’s idea that certain groups of people posed an unacceptable level of risk, and they made the same assessment of certain neighborhoods.

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A subtler form of these ideas is encoded in even the most recent WMDs. Insurance has become ever more personalized since the origins of the industry in the seventeenth century. More data than ever is now available for insurance companies to assess the peculiar risks pertaining to each individual. However, what insurers actually do is to use data “to divide us into smaller tribes.” The opacity of the process prevents us from knowing we are being placed into groups, let alone what those groups are. Some of the criteria are grossly unfair. O’Neil mentions that in Florida, Consumer Reports found that people with poor credit scores and clean driving licenses paid, on average, $1,552 more for car insurance than those with excellent credit scores and a conviction for drunk driving, perhaps the most directly relevant criterion imaginable.

The models place such a high value on credit scores partly because they can be processed quickly and easily. However, the insurance companies are also reluctant to change their practices because they make huge profits from overcharging competent drivers, and without incurring much risk. The opacity of the criteria prevents customers from shopping around for better rates. At the same time that data is withheld from customers, insurers are able to obtain increasing amounts of data about customers. This will eventually allow them to charge prohibitively high rates or refuse coverage to those who pose the highest risk, negating the original purpose of insurance, which was “to help society balance its risk.”

Machines that analyze millions of data points will often find correlations which humans would never consider. This could be something as absurd as “people who spend more than 50 percent of their days on streets starting with the letter J.” It can then take a lot of work (which is not always done) to discover why the machine in question has sorted people into particular categories. Huge amounts of data are fed into black boxes, and as time goes on, we will have less and less idea of the reasoning behind the conclusions that emerge.

Health insurance is particularly vulnerable to the faulty analysis of vast amounts of data. The scores that health insurers use are often based on discredited science, such as the Body Mass Index (BMI), which discriminates against women and those with muscular physiques. Employers are already using health data to assess potential employees and current workers. O’Neil is concerned that “if companies cooked up their own health and productivity models, this could grow into a full-fledged WMD.”

Chapter 10: The Targeted Citizen: Civic Life

When someone posts something on Facebook, they do not know which of their friends will see it. Facebook’s algorithms decide this for users. About two-thirds of adults in America have a Facebook profile, and half of them rely on Facebook “to deliver at least some of their news.” Facebook, therefore, has a huge amount of power to control the news seen by a large segment of the population. Other companies—such as Google, Apple, Microsoft, and Amazon—also control vast quantities of information and are correspondingly powerful. Facebook’s campaign to increase voter turnout in 2010 persuaded an estimated 340,000...

(The entire section contains 1301 words.)

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