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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