Geoffrey C. Barnes

An Experimental Evaluation of the Impact of Intensive Supervision on the Recidivism of High-Risk Probationers

This article reports the results of an experimental evaluation of the impact of Intensive Supervision Probation (ISP) on probationer recidivism. Participants, who were assessed at an increased likelihood of committing serious crimes and not ordered to specialized supervision, were randomly assigned to ISP (n = 447) or standard probation (n = 385). ISP probationers received more restrictive supervision and experienced more office contacts, home visitations, and drug screenings. After 12 months, there was no difference in offending. This equivalence holds across multiple types of crimes, including violent, non-violent, property, and drug offenses, as well as in a survival analysis conducted for each offense type. ISP probationers absconded from supervision, were charged with technical violations, and were incarcerated at significantly higher rates. Policy implications for these results are discussed.

The effects of low-intensity supervision for lower-risk probationers: updated results from a randomized controlled trial

This paper explores the effects of reduced supervision intensity for probationers who were identified, using a random forest forecasting model, as presenting a low risk of committing new serious offenses. It expands on previously reported results of the Philadelphia Low Intensity Community Supervision Experiment, a randomized controlled trial performed from 2007 through 2008. We update our previous one-year recidivism results to include 18 months of follow-up data, and assess additional measures that were not available in earlier analyses, including drug-testing results, officer contact compliance, probation violations, and absconding from supervision. The updated analysis affirms previous findings, showing that reduced supervision intensity does not increase the prevalence or frequency of new offending by low-risk probationers, and does not appear to result in any additional threats to public safety. We conclude that low-intensity supervision, when used in concert with valid and reliable risk forecasting, offers community supervision agencies a powerful tool for managing large offender populations, allowing the agencies to focus scarce resources on higher-risk offenders and perhaps reduce administrative costs. Further research is needed to quantify the exact cost reductions, and to determine the best means of supervising offenders whose risk level makes them ineligible for low-intensity supervision.

Are Restorative Justice Conferences More Fair Than Criminal Courts? Comparing Levels of Observed Procedural Justice in the Reintegrative Shaming Experiments (RISE)

The reintegrative shaming experiments (RISE) were conducted in Canberra, Australia, between 1995 and 2000. RISE compared the effects of standard court proceedings to restorative justice (RJ)–focused diversionary conferences (DCs) with juvenile, young adult, and adult offenders who had been arrested for personal property, shoplifting, violent, or drunk driving offenses. We evaluated, using observational data, the effect of RJ conferences on objective procedural justice. We find that the DCs produced significantly higher levels of offender engagement within the adjudicative process and higher levels of ethical treatment, and that, when compared with standard trials, conduct within the conferences was attuned to the reintegrative shaming (RIS) process. These results reinforce the previous RISE findings by providing evidence that the conferencing process, as delivered, was in keeping with the overall goals of RJ and supports the prior attribution of RISE’s effectiveness to the RJ process.

Algorithmic Risk Assessment Policing Models: Lessons from the Durham HART Model and ‘Experimental’ Proportionality

As is common across the public sector, the UK police service is under pressure to do more with less, to target resources more efficiently and take steps to identify threats proactively; for example under risk-assessment schemes such as ‘Clare’s Law’ and ‘Sarah’s Law’. Algorithmic tools promise to improve a police force’s decision-making and prediction abilities by making better use of data (including intelligence), both from inside and outside the force. This article uses Durham Constabulary’s Harm Assessment Risk Tool (HART) as a case-study. HART is one of the first algorithmic models to be deployed by a UK police force in an operational capacity. Our article comments upon the potential benefits of such tools, explains the concept and method of HART and considers the results of the first validation of the model’s use and accuracy. The article then critiques the use of algorithmic tools within policing from a societal and legal perspective, focusing in particular upon substantive common law grounds for judicial review. It considers a concept of ‘experimental’ proportionality to permit the use of unproven algorithms in the public sector in a controlled and time-limited way, and as part of a combination of approaches to combat algorithmic opacity, proposes ‘ALGO-CARE’, a guidance framework of some of the key legal and practical concerns that should be considered in relation to the use of algorithmic risk assessment tools by the police. The article concludes that for the use of algorithmic tools in a policing context to result in a ‘better’ outcome, that is to say, a more efficient use of police resources in a landscape of more consistent, evidence-based decision-making, then an ‘experimental’ proportionality approach should be developed to ensure that new solutions from ‘big data’ can be found for criminal justice problems traditionally arising from clouded, non-augmented decision-making. Finally, this article notes that there is a sub-set of decisions around which there is too great an impact upon society and upon the welfare of individuals for them to be influenced by an emerging technology; to an extent, in fact, that they should be removed from the influence of algorithmic decision-making altogether.

Even a Little Bit Helps An Implementation and Experimental Evaluation of Cognitive-Behavioral Therapy for High-Risk Probationers

Cognitive-behavioral therapy (CBT) is one of the most promising and widely used therapeutic approaches to reducing recidivism among criminal populations. Although many studies have evaluated CBT for this express purpose, few have done so in a community correctional environment. This article reports findings from a randomized field trial evaluating, “Choosing to Think, Thinking to Choose,” a CBT program designed specifically for a community correctional setting, and its impact on the recidivism of high-risk offenders. High-risk probationers were assigned to either standard, intensive probation (n = 447) or to the treatment condition (n = 457), where they received the same supervision intensity while also being directed to a classroom-based, 14-week CBT program. Twelve months after random assignment, intention-to-treat (ITT) analyses indicate that the overall CBT group was significantly less likely to reoffend, although this effect is concentrated in measures of nonviolent offending.

Targeting Nondomestic Violence Inside Houses of Multi-Occupancy

The issue of indoor, nondomestic violence has been largely ignored in research on hot spots of crime. One prime example in the United Kingdom is a growth of both licensed and unlicensed Houses in Multiple Occupancy (HMOs). These dwellings feature high densities of unrelated adults sharing bathroom, kitchen, and bedroom space. No previous evidence shows whether HMOs have an elevated risk of nondomestic violent crime, but police experience suggests it. The present study used a list of all 47 registered HMOs and all 117 suspected (but unlicensed) HMOs to examine the distribution of 94 nondomestic violent offenses occurring in all 4,401 dwellings in a Berkshire town close to London over calendar year 2013. Eighty-four percent of those indwelling violent offenses occurred in the licensed or suspected HMOs, which constituted 0.4% of all dwellings. The combined HMO rate of 48 violent crimes per 100 dwellings was 137 times higher than the 15 crimes in 4,237 non-HMO dwellings (non-HMO rate of 0.35 crimes per 100 dwellings). Virtually, all of that violence was concentrated in the 117 unlicensed HMOs, which had 78 violent crimes (67 per 100 dwellings), a rate 191 times higher than the non-HMO dwelling rate and 32 times higher than the rate of 1 violent crime in 47 licensed HMOs (2.1 per 100 dwellings). Suspected but unlicensed HMOs may be a prime target for violence prevention through multiagency full enforcement.

Algorithmic risk assessment policing models: Lessons from the Durham Constabulary HART model

ABSTRACT As is common across the public sector, the UK police service is under pressure to do more with less, to target resources more efficiently and take steps to identify threats proactively; for example under risk-assessment schemes such as ‘Clare’s Law’ and ‘Sarah’s Law’. Algorithmic tools promise to improve a police force’s decision-making and prediction abilities by making better use of data (including intelligence), both from inside and outside the force. This article uses Durham Constabulary’s Harm Assessment Risk Tool (HART) as a case-study. HART is one of the first algorithmic models to be deployed by a UK police force in an operational capacity. Our article comments upon the potential benefits of such tools, explains the concept and method of HART and considers the results of the first validation of the model’s use and accuracy. The article then critiques the use of algorithmic tools within policing from a societal and legal perspective, focusing in particular upon substantive common law grounds for judicial review. It considers a concept of ‘experimental’ proportionality to permit the use of unproven algorithms in the public sector in a controlled and time-limited way, and as part of a combination of approaches to combat algorithmic opacity, proposes ‘ALGO-CARE’, a guidance framework of some of the key legal and practical concerns that should be considered in relation to the use of algorithmic risk assessment tools by the police. The article concludes that for the use of algorithmic tools in a policing context to result in a ‘better’ outcome, that is to say, a more efficient use of police resources in a landscape of more consistent, evidence-based decision-making, then an ‘experimental’ proportionality approach should be developed to ensure that new solutions from ‘big data’ can be found for criminal justice problems traditionally arising from clouded, non-augmented decision-making. Finally, this article notes that there is a sub-set of decisions around which there is too great an impact upon society and upon the welfare of individuals for them to be influenced by an emerging technology; to an extent, in fact, that they should be removed from the influence of algorithmic decision-making altogether.