Virginia's robot judge gave sex offender 24 percent shorter sentences
A program in Virginia designed to algorithmically assign risk scores to those convicted of crimes and sentence them according to those scores does not work. Instead, the robot judge—which I fully understand is technically neither anor a judge—allows for harsher sentences on young or black defendants and lighter sentences for sex offenders.
The algorithm was adopted in Virginia in 2002 in order to lower prison populations by predicting the chance of recidivism so that judges could focus on rehabilitation for those with less likelihood of re-offending. Defendants were automatically scored based on offense type, age, prior convictions, employment, and marital status. Soon, 28 other states followed suit.
And though the algorithm could not officially use race as a scoring factor, black defendants were four percent more likely to be sentenced to prison time after the algorithm was implemented and their sentences were 17 percent longer when compared to those of their white counterparts.
Offenders younger than 30 were also scored unfairly, with the automatic addition of 13 points to their risk assessment score, according to the study. This inflated score means that the programs recommend a longer sentence for younger defendants, which seems contradictory to the stated goal of the program, which was focusing on rehabilitation.
“You can have a lot of scenarios where the AI algorithms end up being a rationalization for what the judge wants to do,” University of Maryland professor Frank Pasquale told the. Across Virginia, judges who reported confusion around risk assessment scores unsurprisingly also reported using them inconsistently, with one judge even comparing the algorithm to consulting a psychic.
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