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How to Fight Bias with Predictive Policing 
"Law enforcement's use of predictive analytics recently came under fire again. Dartmouth researchers made waves reporting that simple predictive models—as well as nonexpert humans—predict crime just as well as the leading proprietary analytics software. That the leading software achieves (only) human-level performance might not actually be a deadly blow, but a flurry of press from dozens of news outlets has quickly followed. In any case, even as this disclosure raises questions about one software tool’s credibility, a more enduring, inherent quandary continues to plague predictive policing.

Crime-predicting models are caught in a quagmire doomed to controversy because, on their own, they cannot realize racial equity. It’s an intrinsically unsolvable problem. It turns out that, although such models succeed in flagging (assigning higher probabilities to) both black and white defendants with equal precision, as a result of doing so they also falsely flag black defendants more often than white ones. In this article I cover this seemingly paradoxical predicament and show how predictive policing—more generally, big data in law enforcement—can be turned around to make the legal system fairer in this unfair world."

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