Criminal Dockets, Sentencing, and the Changing Role of Federal Prosecutors

Leipold, Andrew D.

doi:10.1525/fsr.2018.30.3.177

Cited by 1 publication

(1 citation statement)

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“…Recidivism, defined as a person's return to criminal activity after correctional interventions and sanctions, significantly contributes to the mass incarceration issue (Berk 2017;Leipold 2005). Traditional incarceration focuses on punishment rather than addressing the root causes of crime, such as substance use disorder and mental health issues.…”

Section: Introductionmentioning

confidence: 99%

Combining Machine Learning and Queueing Theory for Data-Driven Incarceration-Diversion Program Management

Li,

Castellanos,

Shi

et al. 2024

AAAI

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Incarceration-diversion programs have proven effective in reducing recidivism. Accurate prediction of the number of individuals with different characteristics in the program and their program outcomes based on given eligibility criteria is crucial for successful implementation, because this prediction serves as the foundation for determining the appropriate program size and the consequent staffing requirements. However, this task poses challenges due to the complexities arising from varied outcomes and lengths-of-stay for the diverse individuals in incarceration-diversion programs. In collaboration with an Illinois government agency, we develop a framework to address these issues. Our framework combines ML and queueing model simulation, providing accurate predictions for the program census and interpretable insights into program dynamics and the impact of different decisions in counterfactual scenarios. Additionally, we deploy a user-friendly web app beta-version that allows program managers to visualize census data by counties and race groups. We showcase two decision support use cases: Changing program admission criteria and launching similar programs in new counties.

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Section: Introductionmentioning

confidence: 99%