As a result, dangerous people are out in our communities, while many who could be safely in the community are behind bars. Vast numbers of people accused of petty offenses spend their pretrial detention time jailed alongside hardened convicts, learning from them how to be better criminals.
Ideally, deciding who should stay in jail and who can safely be released would be based on the individual's risk of not showing up for court and his or her risk of committing a crime while awaiting trial. Instead, the deciding factor is often the defendant's pocketbook. Those who have the money for bail -- including those who are more successful criminals -- get out, while the rest remain in jail. Nearly half of Americans don't have $400 for emergencies; if they get arrested, they are probably going to stay in jail until trial or a guilty plea. The long-term economic impact of incarceration is an earnings drop of 40 percent.
In this era of big data, analytics not only can predict and prevent crime but also can discern who should be diverted from jail to treatment for underlying mental health or substance abuse issues. Avoided costs aggregating in the billions could be better spent on detaining high-risk individuals, more mental health and substance abuse treatment, more police officers and other public safety services.
Jurisdictions that do use data to make pretrial decisions have achieved not only lower costs but also greater fairness and lower crime rates. Washington, D.C., releases 85 percent of defendants awaiting trial. Compared to the national average, those released in D.C. are two and a half times more likely to remain arrest-free and one and a half times as likely to show up for court.
Louisville, Ky., implemented risk-based decision-making using a tool developed by the Laura and John Arnold Foundation and now releases 70 percent of defendants before trial. Those released have turned out to be twice as likely to return to court and to stay arrest-free as those in other jurisdictions. Mesa County, Colo., and Allegheny County, Pa., both have achieved significant savings from reduced jail populations due to data-driven release of low-risk defendants.
Data-driven approaches are beginning to produce benefits not only in the area of pretrial detention but throughout the criminal justice process. Dashboards now in use in a handful of jurisdictions allow not only administrators but also the public to see court waiting times by offender type and to identify and address processing bottlenecks.
Fast-tracking minor cases allowed Tarrant County, Texas, for example, to reduce its jail population by 40 percent; mental-health diversion resulted in $10 million in savings per year in Bexar County, Texas; and New Orleans reduced crime while witnessing a two-thirds decrease in its in jail population by using risk-based pretrial decision-making and turning to summonses rather than detention for low-level offenses such as disturbing the peace and marijuana possession.
Why isn't every jurisdiction moving toward data-driven criminal justice decision-making? One of us served as a three-term "tough-on-crime" district attorney and the other as a high-ranking state criminal justice official, yet neither of us saw much semblance of a systemic approach. Change that involves multiple stakeholders is hard and requires dedicated leadership. Only a coordinated system can accurately balance community risk and benefit. There will be mistakes. Someone who is released will commit a serious crime. But large-scale pretrial incarceration of minor offenders just creates more crime.
As more jurisdictions realize that what is fiscally responsible is also just, we have the opportunity not only to save money but also to make our communities safer and economically stronger. Data-driven decision-making might not solve all of the challenges of achieving justice, but it's a good place to start.
This story was originally published by Governing.
