We often hear about the 2.2 million people incarcerated in the United States. Much of the conversation revolves around those in state and federal prisons. But less frequently discussed is a smaller subset of the incarcerated population: the 744,600 Americans held in local jails, more than half of whom have not yet been convicted of a crime, reported The Christian Science Monitor. While some of these pretrial arrestees are considered a threat, many others are detained simply because they can't afford to bail themselves out.
It's a system that favors the rich and punishes the poor, civil rights groups say. Furthermore, studies show that minorities are disproportionately affected by the current bail system: courts are more likely to view African Americans and Latinos as flight risks or public threats, often resulting in higher bail or mandatory pretrial detention.
Now, due to pushback from civil rights advocates and a desire to save government money, an increasing number of courts have begun using computer algorithms to assess risk. Such tools, proponents say, remove any implicit bias from the equation, producing a more objective assessment.
As the pool of research grows and the science of risk assessment becomes more refined, "We actually have increasingly good models of who poses a risk and who doesn't pose a risk," John Pfaff, a professor of law at Fordham University, tells The Washington Post.
The latest pretrial risk assessment tool is the Public Safety Assessment, developed by the Laura and John Arnold Foundation. Drawing from a database of over 1.5 million cases from more than 300 jurisdictions across the US, the algorithm calculates the probability that a defendant will commit a new crime, commit a new violent crime, or fail to return to court.
The assessment takes into consideration a number of factors, including pending charges, prior convictions, whether the current offense is violent, and whether the person has failed to appear at other pretrial hearings. But unlike a human assessor, it's blind to race, gender, level of eduction, socioeconomic status, and neighborhood, all of which can affect a judge's decision, whether subconsciously or consciously.
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