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AI is sending people to jail—and getting it wrong

MIT Technology Review – Using historical data to train risk assessment tools could mean that machines are copying the mistakes of the past. “AI might not seem to have a huge personal impact if your most frequent brush with machine-learning algorithms is through Facebook’s news feed or Google’s search rankings. But at the Data for Black Lives conference last weekend, technologists, legal experts, and community activists snapped things into perspective with a discussion of America’s criminal justice system. There, an algorithm can determine the trajectory of your life. The US imprisons more people than any other country in the world. At the end of 2016, nearly 2.2 million adults were being held in prisons or jails, and an additional 4.5 million were in other correctional facilities. Put another way, 1 in 38 adult Americans was under some form of correctional supervision. The nightmarishness of this situation is one of the few issues that unite politicians on both sides of the aisle. Under immense pressure to reduce prison numbers without risking a rise in crime, courtrooms across the US have turned to automated tools in attempts to shuffle defendants through the legal system as efficiently and safely as possible. This is where the AI part of our story begins.

Police departments use predictive algorithms to strategize about where to send their ranks. Law enforcement agencies use face recognition systems to help identify suspects. These practices have garnered well-deserved scrutiny for whether they in fact improve safety or simply perpetuate existing inequities. Researchers and civil rights advocates, for example, have repeatedly demonstrated that face recognition systems can fail spectacularly, particularly for dark-skinned individuals—even mistaking members of Congress for convicted criminals But the most controversial tool by far comes after police have made an arrest. Say hello to criminal risk assessment algorithms…” [h/t Pete Weiss]

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