Fahad Diwan logs in and fills out the details of a person facing a bail hearing. Date of birth. Gender. Current charges. Pending charges. Past convictions.Once his SmartBail program is done, he says, an algorithm trained on a mountain of data will be able to assess whether that suspect is a good candidate for pretrial release. Unlikely to be a flight risk. Unlikely to commit offences. Likely to comply with the conditions of release.Suspects in custody are âlegally innocent people,â said Diwan, 30, who hopes to one day put his software to use in Ontarioâs bail courts. âWe just want to find a way to make the system better, faster, economical.âProponents of this kind of program say machine learning would save time and money by quickly identifying people who should be released, speeding up bail hearings, reducing the number of people in jails and freeing up courts to focus on defendants who should have a full, contested hearing. All that with less bias and without affecting the crime rate.At least, thatâs how it should work. Some researchers argue that risk-assessment algorithms can replicate bias already present in the criminal-justice system. Advocates are also concerned about the accuracy, reliability and transparency of such tools, as well as data collection and privacy.Despite calls for caution, bail algorithms are now in use in certain U.S. states, including New Jersey, where a tool has resulted in more pretrial releases. Machine-learning software such as the proprietary COMPAS (Correctional Offender Management Profiling for Alternative Sanctions) tool are also being used at sentencing hearings.It is only a matter of time before machine-learning technology will make its way into Canadian bail courts, experts say, and we need to be prepared.The efficiency promised by artificial intelligence may be especially seductive in the wake of Legal Aid Ontario budget cuts by the Ford government. Those cuts led to a much-criticized deci ...
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