Decrease the percentage of offenders returning to prison within 36 months of release from 21.2% in 2013 to 20.1% by 2017.

This dataset provides information on the number of individuals released between the years of 2011-2015 and the number of individuals re-incarcerated. Below are a couple of items to note about the dataset: Recidivism is defined as a return to incarceration within three years of the formerly incarcerated individuals date of release from a state correctional institution

NIJ's Recidivism Challenge - Data Provided by Georgia Department of Community Supervision, Georgia Crime Information Center. The Challenge uses data on roughly 26,000 individuals from the State of Georgia released from Georgia prisons on discretionary parole to the custody of the Georgia Department of Community Supervision (GDCS) for the purpose of post-incarceration supervision between January 1, 2013 and December 31, 2015. This is the dataset of all individuals (training and test) with all variables released.

Recidivism is the act of committing another crime or coming into conflict with the criminal justice system (CJS) again. It is an important measure of the effectiveness of CJS efforts to promote rehabilitation, reintegration, and public safety. This fact sheet is based on publicly available data from the provincial governments of Ontario and Québec, the Correctional Service of Canada (CSC), Public Safety Canada (PSC), and Statistics Canada. The data were collected from 2001 to 2016.

Port of the compas-recidivism dataset from propublica (github here). See details there and use carefully, as there are serious known social impacts and biases present in this dataset. Basic preprocessing done by the imodels team in this notebook. The target is the binary outcome is_recid.

  Sample usage

Load the data: from datasets import load_dataset

dataset = load_dataset("imodels/compas-recidivism") df = pd.DataFrame(dataset['train']) X = df.drop(columns=['is_recid']) y =… See the full description on the dataset page: https://huggingface.co/datasets/imodels/compas-recidivism.

The purpose of the study was to quantify the effect of the embrace of DNA technology on offender behavior. In particular, researchers examined whether an offender's knowledge that their DNA profile was entered into a database deterred them from offending in the future and if probative effects resulted from DNA sampling. The researchers coded information using criminal history records and data from Florida's DNA database, both of which are maintained by the Florida Department of Law Enforcement (FDLE), and also utilized court docket information acquired through the Florida Department of Corrections (FDOC) to create a dataset of 156,702 cases involving offenders released from the FDOC in the state of Florida between January 1996 and December 2004. The data contain a total of 50 variables. Major categories of variables include demographic variables regarding the offender, descriptive variables relating to the initial crime committed by the offender, and time-specific variables regarding cases of recidivism.

NIJ's Recidivism Challenge - Data Provided by Georgia Department of Community Supervision, Georgia Crime Information Center. The initial test dataset is the remaining 30% of the population used in the Challenge. This dataset does not have the dependent variable as that is what you are intended to forecast.

The rate of recidivism of sentenced prisoners in Finland fluctuated over the period from 2009 to 2018. 76 percent of sentenced prisoners released in 2018 who had six or more previous prison sentences returned to prison within a five-year follow-up period. The corresponding share among prisoners with one previous sentence was 41 percent.

Three different measures of recidivism (rearrest, reincarceration, and overall recidivism) have been used by the DOC in a recent report to further explore the effects of recidivism on the criminal justice system. The DOC defines rearrest as: “the first instance of arrest after the individual is released from the custody of the DOC.” The DOC defines reincarceration as: “the first instance of returning to the custody of the DOC after the individual is released from the DOC.” The DOC defines overall recidivism as: “the first instance of any type of rearrest or reincarceration after the individual is released from the DOC.”

• The 3-year rearrest rates have been consistently higher than the 3-year reincarceration rates.
• The overall recidivism rates have remained relatively stable.
• From 2000 to 2013, rearrest rates appear to grow consistently before trending downward in recent years (e.g. 3-year rate has been below 50.0% in 4 of the last 5 years).
• The 2013 3-year reincarceration rate was down slightly at 43.7%, still lower than all the 3-year rates prior to 2008.
• The 2013 3-year overall recidivism rate dropped to 60.9%. This was the second lowest overall recidivism rate in the past 13 years (with 2011 being lowest at 59.9%)
• The 2015 1-year rates were all up slightly.

These statistics are based on a cohort of inmates.

This data collection examines the relationship between individual characteristics and recidivism for two cohorts of inmates released from North Carolina prisons in 1978 and 1980. The survey contains questions on the background of the offenders, including their involvement in drugs or alcohol, level of schooling, nature of the crime resulting in the sample conviction, number of prior incarcerations and recidivism following release from the sample incarceration. The data collection also contains information on the length of time until recidivism occurs.

Under the Personal Responsibility and Work Opportunity Reconciliation Act (PRWORA) of 1996, individuals convicted of drug-related felonies were permanently banned from receiving welfare and food stamps. Since then, over 30 states have opted out of the federal ban. In this paper, I estimate the impact of public assistance eligibility on recidivism by exploiting both the adoption of the federal ban and subsequent passage of state laws that lifted the ban. Using administrative prison records on five million offenders and a triple-differences research design, I find that public assistance eligibility for drug offenders reduces one-year recidivism rates by 10 percent.

The rate of recidivism among released prisoners in Finland fluctuated over the period from 2009 to 2018. 60 percent of sentenced prisoners released in 2018 returned to prison within a five-year follow-up period.

In France in 2022, the legal recidivism and reoffending rate among those convicted of offenses was **** percent. Between 2011 and 2021, the recidivism rate of offenders remained stable: between **** and **** percent. Nevertheless, in 2022 this figure increased significantly compared to the previous year.

The rate of recidivism among sentenced prisoners in Finland fluctuated between 2009 and 2018. 61 percent of male prisoners and 45 percent of female prisoners released in 2018 returned to prison within a five-year follow-up period.

ObjectiveThere is currently inconclusive evidence regarding the relationship between recidivism and mental illness. This retrospective study aimed to use rigorous machine learning methods to understand the unique predictive utility of mental illness for recidivism in a general population (i.e.; not only those with mental illness) prison sample in the United States.MethodParticipants were adult men (n = 322) and women (n = 72) who were recruited from three prisons in the Midwest region of the United States. Three model comparisons using Bayesian correlated t-tests were conducted to understand the incremental predictive utility of mental illness, substance use, and crime and demographic variables for recidivism prediction. Three classification statistical algorithms were considered while evaluating model configurations for the t-tests: elastic net logistic regression (GLMnet), k-nearest neighbors (KNN), and random forests (RF).ResultsRates of substance use disorders were particularly high in our sample (86.29%). Mental illness variables and substance use variables did not add predictive utility for recidivism prediction over and above crime and demographic variables. Exploratory analyses comparing the crime and demographic, substance use, and mental illness feature sets to null models found that only the crime and demographics model had an increased likelihood of improving recidivism prediction accuracy.ConclusionsDespite not finding a direct relationship between mental illness and recidivism, treatment of mental illness in incarcerated populations is still essential due to the high rates of mental illnesses, the legal imperative, the possibility of decreasing institutional disciplinary burden, the opportunity to increase the effectiveness of rehabilitation programs in prison, and the potential to improve meaningful outcomes beyond recidivism following release.

This dataset provides references for 99 recidivism studies conducted between 1995-2009 in all 50 states and the District of Columbia. The studies have been produced by a variety of agencies, including departments of corrections, sentencing commissions, statistical analysis centers, and universities. The analyses addresses a broad variety of issues, including juvenile/adult status, gender, race, type of offense, type of program intervention, and many others. Because of this diversity, measurements of recidivism rates are not necessarily comparable across jurisdictions, but overall the studies provide insight into the variety of factors that affect recidivism for people sentenced to incarceration or community supervision.

Recidivism is the act of an individual leaving prison (parole/special sentence, work release, or discharge) who is reincarcerated within three years for any reason.

The key metric of recidivism for Maryland and for the Department of Public Safety and Correctional Services (DPSCS) is the cumulative percentage of releasees who return to Corrections within three years. Since the start of the O'Malley-Brown administration we have driven down recidivism rates at a record pace. This dataset shows how many releasees return to Corrections within one, two, and three years of release. DPSCS's Office of Grants Policy and Statistics (GPS) publishes these data annually in their Repeat Incarceration Supervision Cycle (RISC) reports. The newest data were published in August 2013. Data for FY 2012 are scheduled to be released in summer 2014.

In France in 2022, the legal recidivism and reoffending rate among those convicted of crimes was *** percent, a decrease compared to the previous year. Between 2011 and 2022, the recidivism rate of criminals fluctuated between *** and **** percent.