Data Mining: The Dangerous Future of Affirmative Action
Ever since conservative courts and voters began trying to eliminate affirmative action in the 1990s, universities have sought creative ways to boost their enrollment of minority students without explicitly relying on race. When California voters banned racial preferences in public universities in 1996, for example, the University of California responded by adopting admissions preferences based on socioeconomic status instead. And after a federal appellate court struck down the University of Texas’s race-based affirmative action program, the school adopted a plan that guaranteed admission to those students graduating in the top 10 percent of their high school class.
When the Texas effort—known as the Top Ten Percent Plan—failed to generate the racial diversity school officials sought, the university returned to using explicit racial preferences. Those preferences are now being challenged in the Supreme Court case of Fisher v. Texas, and many expect the conservative justices to deal what could be a fatal blow to race-based affirmative action at American public universities. Once again, however, the universities have a secret weapon they hope will allow them to circumvent such a ruling: data mining.
Whether it’s used in airport security or online advertising or education, data mining works by finding patterns and correlations. Based on census data, the spending patterns of my neighbors, and my Washington, D.C., ZIP code 20016, the Nielsen Company classifies me as someone who lives among the “Young Digerati”—that is, high-income consumers who are “tech-savvy and live in fashionable neighborhoods on the urban fringe.” My fellow Washingtonians a few miles to the southeast in Anacostia are categorized using very different terms. They are the “Big City Blues,” a community of “low-income Asian and African-American households occupying older inner-city apartments.” Based on where we live and what we spend, Nielsen creates aggregate predictions about our likely buying habits so that advertisers can send us ads that reflect our interests. That’s a little creepy—but then again, we’re talking about advertising. To some education experts, however, data mining also represents the future of public education.