Correctional Offender Management Profiles for Alternative Sanctions (<scp>COMPAS</scp>)

Brennan, Tim; Dieterich, William

doi:10.1002/9781119184256.ch3

Cited by 18 publications

(14 citation statements)

References 23 publications

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“…The time between arrest and court appearances for pretrial defendants is much shorter than other criminal justice populations. As mentioned previously, prediction research involving convicted offender populations often utilize follow-up periods of 1 year or more (for examples, see Austin et al, 2003; Brennan et al, 2009; Brennan et al, 2004; Coulson et al, 1996; Van Voorhis et al, 2010). However, it should be noted that statistically significant pathways were apparent despite low base rates.…”

Section: Discussionmentioning

confidence: 99%

“…Some correctional risk/needs assessments have already begun to incorporate this hybrid model. Assessments like the WRNA (Van Voorhis et al, 2010), the COMPAS Reentry Assessment (Brennan & Dieterich, 2007), and the Service Planning Instrument for Women (SPIn-W; Robinson, Van Dieten, & Millison, 2005) utilize gender-responsive risk factors to achieve more accurate pictures of women offenders. The results from the current study suggest that abuse and trauma, mental health, and substance abuse are salient risk factors for women and are interconnected in a way that is different than their male counterparts.…”

Section: Discussionmentioning

confidence: 99%

“…Dichotomous outcome measures were developed to capture FTAs (0 = no, 1 = yes), and new arrests (0 = no, 1 = yes) at 6 months following the data collection. 8 While prediction research involving offender populations often utilizes follow-up periods of 1 year or more (for examples, see Austin, Coleman, Peyton, & Dedel Johnson, 2003; Brennan, Dieterich, & Ehret, 2009; Brennan, Dieterich, & Oliver, 2004; Coulson, Ilacqua, Nutbrown, & Cudjoe, 1996; Girard & Wormith, 2004; Hollin & Palmer, 2006; Simourd, 2004; Van Voorhis et al, 2010), the average length of time of a defendant on pretrial release reflects a very limited time period. This time period can range from 3 to 9 months, depending upon the category (i.e., misdemeanor or felony) of the alleged charge (Piedmont Triad Regional Council, 2012).…”

Section: Methodsmentioning

confidence: 99%

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A Direct Test of Pathways Theory

Gehring

2016

Feminist Criminology

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Although qualitative research has informed Pathways Theory for decades, there are few quantitative studies that have explored this theory. This study utilizes a path analytic approach with measures of gender-responsive needs collected from 163 male and 103 female pretrial defendants. Results indicate the presence of a pathway to pretrial failure that includes childhood abuse, historical indicators of mental illness, and substance abuse for women only. The implications of this study are likely to inform the current practice of correctional risk/needs assessments and case planning, and to aid the development of more comprehensive and effective programs and interventions for justice-involved women.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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A Direct Test of Pathways Theory

Gehring

2016

Feminist Criminology

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“…49 While Broward County officials administered COMPAS in a pretrial setting, the tool is also employed by various jurisdictions in sentencing decisions. 50 The illustrations herein focus on the COMPAS algorithm that predicts violent offending specifically (COMPAS also offers general recidivism and pretrial algorithms) because, as discussed earlier, a tool that predicts only serious offending might be perceived as providing a better fit for the purposes of sentencing.…”

Section: Accuracymentioning

confidence: 99%

Judicial gatekeeping on scientific validity with risk assessment tools

Hamilton

2020

Behavioral Sci & The Law

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Risk assessment tools driven by algorithms offer promising advantages in predicting the recidivism risk of defendants.Jurisdictions are increasingly relying upon risk tool outcomes to help judges at sentencing with their decisions on whether to incarcerate or whether to use community-basedsanctions. Yet as sentencing has significant consequences for public safety and individual rights, care must be taken that the tools relied upon are appropriate for the task.Judges are encouraged to act as gatekeepers to evaluate whether the forensic risk assessment tool offered has a sufficient level of validity in that it is fit for the purposes of sentencing, provides an acceptable level of accuracy in its predictions, and achieves an adequate standard of reliability with regard to its outcomes.

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“…The increasing use of machine learning models [1] is expected to bring several benefits [2][3][4], including improvements in the efficiency of processes, enhancements in the experience of users of products and services, and the development of potentially more impartial decision making systems. A proof of the value added by this technology is the number of fields in which it is already being applied, including healthcare [5], law enforcement [6], financial services [7], employment [8], cybersecurity [9], and autonomous vehicles [10].…”

Section: Introductionmentioning

confidence: 99%

The Challenges of Machine Learning and Their Economic Implications

Borrellas

Unceta

2021

Entropy

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The deployment of machine learning models is expected to bring several benefits. Nevertheless, as a result of the complexity of the ecosystem in which models are generally trained and deployed, this technology also raises concerns regarding its (1) interpretability, (2) fairness, (3) safety, and (4) privacy. These issues can have substantial economic implications because they may hinder the development and mass adoption of machine learning. In light of this, the purpose of this paper was to determine, from a positive economics point of view, whether the free use of machine learning models maximizes aggregate social welfare or, alternatively, regulations are required. In cases in which restrictions should be enacted, policies are proposed. The adaptation of current tort and anti-discrimination laws is found to guarantee an optimal level of interpretability and fairness. Additionally, existing market solutions appear to incentivize machine learning operators to equip models with a degree of security and privacy that maximizes aggregate social welfare. These findings are expected to be valuable to inform the design of efficient public policies.

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Correctional Offender Management Profiles for Alternative Sanctions (COMPAS)

Cited by 18 publications

References 23 publications

A Direct Test of Pathways Theory

A Direct Test of Pathways Theory

Judicial gatekeeping on scientific validity with risk assessment tools

The Challenges of Machine Learning and Their Economic Implications

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