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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