In 2013, Eric Loomis was sentenced to six years in prison and five more years of parole because a criminal risk predictor software rated his profile as high recidivism.<\/p>\n\n\n\n
This happened despite the fact that he was charged with misdemeanors<\/strong>: trying to flee from a traffic officer and driving a car without the owner’s consent.<\/p>\n\n\n\n Three years later, the case went to the Wisconsin Supreme Court for review of the sentence.<\/p>\n\n\n\n Juliana Vivar Vera<\/strong><\/a>, professor at Tec de Monterrey’s School of Social Sciences and Government<\/strong><\/a>, explains that the need to perfect this type of software and train judges to use it as a support tool are the factors that have halted its implementation in other countries.<\/p>\n\n\n\n Loomis’ defense argued that the software had violated the defendant’s \u201cdue process<\/strong>\u201d by using a predictive algorithm that calls into question a person’s presumption of innocence.<\/p>\n\n\n\n Moreover, the use of a risk predictor biases the sentencing of a person without giving the defendant the opportunity to prove otherwise.<\/p>\n\n\n\n And something else. The methodology used by the software to predict risk is \u201ca trade secret\u201d and the machine is only required to report recidivism estimates to a judge.<\/p>\n\n\n\n That lack of transparency would be sufficient to challenge the sentence, the defense argued.<\/p>\n\n\n\n The Loomis case also brought up the fact that these machines were more likely to give higher risk ratings to offenders of color than to white offenders.<\/p>\n\n\n\n These are programs developed using machine learning<\/em> methodology<\/strong>. This means that by using the data and statistics fed into the system, the predictor is able to perform increasingly accurate cross-referencing of information that, in theory and in judicial matters, should give sentences appropriate to the crime committed.<\/p>\n\n\n\n A Harvard Law School \u201cState v. Loomis<\/em><\/strong><\/a>\u201d analysis warns that judges should be a bias check on an artificial intelligence tool designed, precisely, \u201cto correct for judges’ biases.\u201d<\/p>\n\n\n\n In an interview with TecScience<\/em><\/strong>, Vivar Vera explains that training the judicial system in this area is one of the considerations that needs to be taken into account when using this type of technology in countries such as Mexico.<\/p>\n\n\n\n \u201cJudges couldn’t make a decision with the help of an algorithmic predictor that they don’t understand,\u201d warns the author of the article \u201c<\/a>The Criminal Sentence, the Judge, and the Algorithm: Will New Technologies be Our New Judges?<\/em><\/a>\u201d<\/a>.<\/p>\n\n\n\n \u201cThere needs to be an understanding and an assurance from the state that judges will be trained and will be familiar with such expert machines and know that they can truly be an auxiliary or assistant,\u201d she says.<\/p>\n\n\n\n The researcher adds that the use of this type of predictor was seen as an \u201caid,\u201d but in the cases where it has been used, judges issued sentences without questioning the results of artificial intelligence, which led to errors that need to be corrected.<\/p>\n\n\n\n \u201c(The judge) trusted the machine more than his or her own human judgment,\u201d the professor says.<\/p>\n\n\n\n<\/a>Justice with artificial intelligence <\/strong><\/strong><\/h2>\n\n\n\n