When a person lands up in jail, he will usually apply for bail so he does not have to stay in jail pending trial. A bail is cash or surety given by an arrested person or someone on his behalf to ensure he will appear before the court as and when needed. If the person out on bail does not appear before the court as directed, the court will forfeit the bail amount and issue a warrant for his re-arrest.
Apart from certain minor offenses carrying predetermined bail amounts, judges must carefully weigh all the considerations before allowing bail to the applicant. While judges use their experience and understanding of the arrested person’s profile, criminal history, community standing, flight risk, and the seriousness of the crime to decide bail approval and amount, the matter remains fundamentally subjective. As a result, many courts in America are turning to advanced artificial intelligence technology to evaluate the risks of the pretrial release of defendants.
What Factors Do Bail Algorithms Consider?
Bail algorithms are mathematical formulas employing statistics to assess the risk of the accused not appearing in court for trial. The algorithm considers specific information about the defendant to provide a scientific assessment free from personal biases. The judge gets the opportunity to review the result turned up by the algorithm before deciding on the bail application.
A bail algorithm considers multiple factors to produce a score a judge can use as a recommendation for or against pretrial release. The factors include the defendant’s age, the charges, his criminal record, a record of jumping bail, his influence, the propensity of committing acts of violence, risk of flight, and more. The algorithm also cuts down the time it takes to get bailed out.
The judge can evaluate scores on different aspects. For example, the algorithm may give one score for the defendant jumping bail and another to reflect the chance of the defendant committing another crime when out on bail. Some algorithms may also indicate the defendant’s probability of committing violent acts if released on bail.
Benefits of Bail Algorithms
Many states in the U.S. have implemented bail algorithms to eliminate or minimize the use of cash bail because of the common perception of bail being unfair to people from minority and poorer communities who end up spending time in jail, even for minor charges. The bail algorithm eliminates the personal biases of a single judge in the decision-making process but combines statistics-based mathematical formulas with human judgment born of experience for a fairer outcome.
Criticisms of Bail Algorithms
Critics of bail algorithms have questioned the precept of fairness because the algorithms do not consider some factors like history of substance abuse or employment status. Some people also allege that the algorithms focus more on the nature of the alleged offense, not the underlying facts. According to Wired, a more serious allegation is that the bail algorithms discriminate against people of color.
On the face of it, data-based bail algorithms appear to be fairer than the arbitrary decisions of a judge, however, it does seem that the formula needs more tweaking to make it better and more relevant.