Too Short for a Blog Post, Too Long for a Tweet 176

Here are a couple of excerpts from a book I recently read, "Weapons of Math Destruction  How Big Data Increases Inequality and Threatens Democracy," by Cathy O'Neil:

Students in the Chinese city of Zhongxiang had a reputation for acing the national standardized test, or gaokao, and winning places in China’s top universities. They did so well, in fact, that authorities began to suspect they were cheating. Suspicions grew in 2012, according to a report in Britain’s Telegraph, when provincial authorities found ninety-nine identical copies of a single test. 

The next year, as students in Zhongxiang arrived to take the exam, they were dismayed to be funneled through metal detectors and forced to relinquish their mobile phones. Some surrendered tiny transmitters disguised as pencil erasers. Once inside, the students found themselves accompanied by fifty-four investigators from different school districts. A few of these investigators crossed the street to a hotel, where they found groups positioned to communicate with the students through their transmitters. 

The response to this crackdown on cheating was volcanic. Some two thousand stone-throwing protesters gathered in the street outside the school. They chanted, “We want fairness. There is no fairness if you don’t let us cheat.” 

It sounds like a joke, but they were absolutely serious. The stakes for the students were sky high. As they saw it, they faced a chance either to pursue an elite education and a prosperous career or to stay stuck in their provincial city, a relative backwater. And whether or not it was the case, they had the perception that others were cheating. So preventing the students in Zhongxiang from cheating was unfair. In a system in which cheating is the norm, following the rules amounts to a handicap.

Data is not going away. Nor are computers—much less mathematics. Predictive models are, increasingly, the tools we will be relying on to run our institutions, deploy our resources, and manage our lives. But as I’ve tried to show throughout this book, these models are constructed not just from data but from the choices we make about which data to pay attention to—and which to leave out. Those choices are not just about logistics, profits, and efficiency. They are fundamentally moral.