Superfreakonomics_ global cooling, patri - Steven D. Levitt [42]
Nor, of course, could the authorities handle the workload.
This is a common problem in health care. A review of a recent cancer-screening trial showed that 50 percent of the 68,000 participants got at least 1 false-positive result after undergoing 14 tests. So although health-care advocates may urge universal screening for all sorts of maladies, the reality is that the system would be overwhelmed by false positives and the sick would be crowded out. The baseball player Mike Lowell, a recent World Series MVP, underscored a related problem while discussing a plan to test every ballplayer in the league for human growth hormone. “If it’s 99 percent accurate, that’s going to be 7 false positives,” Lowell said. “What if one of the false positives is Cal Ripken? Doesn’t it put a black mark on his career?”
Similarly, if you want to hunt terrorists, 99 percent accurate is not even close to good enough.
On July 7, 2005, four Islamic suicide bombers struck in London, one on a crowded bus and three in the Underground. The murder toll was fifty-two. “Personally, I was devastated by it,” Horsley recalls. “We were just starting to work on identifying terrorists and I thought maybe, just maybe, if we had started a couple years earlier, would we have stopped it?”
The 7/7 bombers left behind some banking data, but not much. In the coming months, however, a flock of suspicious characters accommodated our terrorist-detection project by getting themselves arrested by the British police. Granted, none of these men were proven terrorists; most of them would never be convicted of anything. But if they resembled a terrorist closely enough to get arrested, perhaps their banking habits could be mined to create a useful algorithm. As luck would have it, more than a hundred of these suspects were customers at Horsley’s bank.
The procedure would require two steps. First, assemble all the available data on these hundred-plus suspects and create an algorithm based on the patterns that set these men apart from the general population. Once the algorithm was successfully fine-tuned, it could be used to dredge through the bank’s database to identify other potential bad guys.
Given that the United Kingdom was battling Islamic fundamentalists and no longer, for instance, Irish militants, the arrested suspects invariably had Muslim names. This would turn out to be one of the strongest demographic markers for the algorithm. A person with neither a first nor a last Muslim name stood only a 1 in 500,000 chance of being a suspected terrorist. The likelihood for a person with a first or a last Muslim name was 1 in 30,000. For a person with first and last Muslim names, however, the likelihood jumped to 1 in 2,000.
The likely terrorists were predominately men, most commonly between the ages of twenty-six and thirty-five. Furthermore, they were disproportionately likely to:
Own a mobile phoneBe a studentRent, rather than own, a home
These traits, on their own, would hardly be grounds for arrest. (They describe just about every research assistant the two of us have ever had, and we are pretty sure none of them are terrorists.) But, when stacked atop the Muslim-name markers, even these common traits began to add power to the algorithm.
Once the preceding factors were taken into account, several other characteristics proved fundamentally neutral, not identifying terrorists one way or another. They included:
Employment statusMarital statusLiving in close proximity to a mosque
So contrary to common perception, a single, unemployed, twenty-six-year-old man who lived next door to a mosque was no more likely to be a terrorist than another twenty-six-year-old