Wired has a longer look at researchers who’ve boldly taken on the thankless task of taking on the whole “voter ID” controversy with real data on how the laws work and what the arguments get wrong:
Recently, however, researchers at Tufts University and Harvard University demonstrated that it’s possible to match individuals across government databases with nearly perfect accuracy, using just a few basic identifiers like a person’s name, date of birth, and address. They developed the algorithm while working as expert witnesses in the Department of Justice’s case against Texas. Now, in a newly published paper, researchers Stephen Ansolabehere of Harvard and Eitan Hersh of Tufts have explained the underlying methodology. Their goal, according to Hersh, is to create a system courts can easily understand, which can not only be used in future voter ID law cases, but can also help dispel some myths about who those laws do and don’t hurt.
So many algorithms that purport to match people across databases run up against the so-called black box problem. They may be able to make statistically sound decisions, but they can’t easily explain how they made them. In a recent Supreme Court hearing over partisan gerrymandering in Wisconsin, Chief Justice John Roberts dismissed research-backed methods to measure gerrymandering as “sociological gobbledygook.” Hersh and Ansolabehere wanted to develop a tool that could be easily understood.
So, working with the Department of Justice, the researchers set out to determine whether they could match voters on the voter roll with their corresponding records in ID databases using just a few basic details. To do that, they developed an algorithm that scanned the state of Texas’s voter rolls and compared it to the federal list of driver licenses, state IDs, and concealed handgun permits, among other forms of acceptable identification. It scanned each record by address, date of birth, gender, and name, to see if, for instance, a combination of address, gender, and name would be as accurate a predictor as a combination of date of birth, gender, and name.
To check their results, the researchers relied on a subset of the voter data that contained Social Security numbers. Those records effectively served as the algorithm’s answer key. They ultimately found that 98 percent of the records that could be matched using Social Security numbers could also be matched using any three of the four key identifiers—address, date of birth, gender, and name.
“This combination is as good as a Social Security number,” Hersh says.
The algorithm shows a clear and disturbing racial disparity on voting rights. But Hersh says that it also shows that voter ID laws affect a relatively small percentage of the population. Across all registered voters in Texas, the researchers found 4.5 percent lack proper identification. For registered voters who actually showed up at the polls in 2012, it’s 1.5 percent.
“You’re down to a small percentage of the population that doesn’t have an ID,” says Hersh. That’s one reason why, despite Alabama’s restrictive voter ID law, black turnout in the recent Senate election still exceeded expectations. Still, while the percentages may sound small, that 4.5 percent still represents 608,470 Texas citizens who could potentially be disenfranchised.