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Evaluating causes of algorithmic bias in juvenile criminal recidivism

Marius Miron, Songül Tolan, Emilia Gómez, Carlos Castillo
2020 Artificial Intelligence and Law  
We explore in more detail two possible causes of this algorithmic bias that are related to biases in the data with respect to two protected groups, foreigners and women.  ...  In this paper we investigate risk prediction of criminal re-offense among juvenile defendants using general-purpose machine learning (ML) algorithms.  ...  Methodology Here we propose a methodology to study the causes of algorithmic discrimination when using common ML classification algorithms to predict juvenile criminal recidivism.  ... 
doi:10.1007/s10506-020-09268-y fatcat:ysmn2vrujngltke5bhst4leo3y

Social Determinants of Recidivism: A Machine Learning Solution [article]

Vik Shirvaikar, Choudur Lakshminarayan
2021 arXiv   pre-print
In criminal justice analytics, the widely-studied problem of recidivism prediction (forecasting re-offenses after release or parole) is fraught with ethical missteps.  ...  In particular, Machine Learning (ML) models rely on historical patterns of behavior to predict future outcomes, engendering a vicious feedback loop of recidivism and incarceration.  ...  One area of long-standing interest is the use of algorithms to assess criminals' risk of recidivism.  ... 
arXiv:2011.11483v3 fatcat:shox2fdchveitnig3awykb3nna

Neuroprediction and A.I. in Forensic Psychiatry and Criminal Justice: A Neurolaw Perspective

Leda Tortora, Gerben Meynen, Johannes Bijlsma, Enrico Tronci, Stefano Ferracuti
2020 Frontiers in Psychology  
techniques in the fields of forensic psychiatry and criminal justice, considering legal implications and ethical issues.  ...  This technique could be referred to as 'A.I. neuroprediction,' and involves identifying potential neurocognitive markers for the prediction of recidivism.  ...  use of neuroimaging in courts is at risk of being misleading, due to cognitive biases in the evaluation of evidence (Scarpazza et al., 2018) .  ... 
doi:10.3389/fpsyg.2020.00220 pmid:32256422 pmcid:PMC7090235 fatcat:jgaj356yfvfbja4pbk4msqjjfa

Human Perceptions of Fairness in Algorithmic Decision Making: A Case Study of Criminal Risk Prediction [article]

Nina Grgić-Hlača, Elissa M. Redmiles, Krishna P. Gummadi, Adrian Weller
2018 arXiv   pre-print
As algorithms are increasingly used to make important decisions that affect human lives, ranging from social benefit assignment to predicting risk of criminal recidivism, concerns have been raised about  ...  in decision-making algorithms.  ...  First, evaluations of most latent properties related to causality, namely causes vicious cycles, caused by sensitive features, causes disparity in outcomes, causal relationship with outcomes, and volitionality  ... 
arXiv:1802.09548v1 fatcat:5hnw24nwrrdn7ncwticwkjsknm

The effect of prison gang membership on recidivism

Brendan D. Dooley, Alan Seals, David Skarbek
2014 Journal of criminal justice  
Results: Prison gang membership results in a six percentage point increase in recidivism.  ...  Conclusions: Despite the strengths of the data, unobserved heterogeneity among inmates could still bias estimates.  ...  One of the strengths of the data-its origins in a chronic gang state-may simultaneously cause estimation bias because of unobserved heterogenity in the prison gang members.  ... 
doi:10.1016/j.jcrimjus.2014.01.002 fatcat:ibc3mfoqz5ftnc2zwb5zxzadti

Local spatial biclustering and prediction of urban juvenile delinquency and recidivism

Alan J. Izenman, Philip W. Harris, Jeremy Mennis, Joseph Jupin, Zoran Obradovic
2011 Statistical analysis and data mining  
To address the difficult issue of nonstationarity in the data, we apply the plaid biclustering algorithm in which a sequence of subsets ("layers") of both juveniles and variables are extracted from the  ...  Results show substantial improvements in predicting juvenile recidivism using the methods of this paper. Justice.  ...  Plaid Models and Algorithm The "plaid" biclustering algorithm (Lazzeroni and Owen, 2002) Let X ij denote the value of the ith juvenile measured on the jth variable.  ... 
doi:10.1002/sam.10123 fatcat:oqexys3r4ngxrlxaptpvtlx6ru

Measuring Change: From Rates of Recidivism to Markers of Desistance

Cecelia M. Klingele
2018 Social Science Research Network  
This Article suggests that, however popular, recidivism alone is a poor metric for gauging the success of criminal justice interventions or of those who participate in them.  ...  The system's success is frequently judged by the recidivism rates of those who are subject to various criminal justice interventions, from treatment programs to imprisonment.  ...  impact evaluation in juvenile justice without measuring recidivism.  ... 
doi:10.2139/ssrn.3142405 fatcat:oh72rmayunb5hbmi6iuq3r2baa

A Meta-Analysis of Moral Reconation Therapy

L. Myles Ferguson, J. Stephen Wormith
2012 International journal of offender therapy and comparative criminology  
Recipients of MRT included adult and juvenile offenders who were in custody or in the community, typically on parole or probation.  ...  Moderator analysis demonstrated that MRT was more successful with adult than juvenile offenders in institutional settings as opposed to the community, and where researchers in the primary studies used  ...  a moral education component in juvenile offender treatment to reduce recidivism (Gibbs, 1995) .  ... 
doi:10.1177/0306624x12447771 pmid:22744908 fatcat:jcbtnih42faspjguqc3y3ib7ai

Crowdsourcing Perceptions of Fair Predictors for Machine Learning

Niels van Berkel, Jorge Goncalves, Danula Hettiachchi, Senuri Wijenayake, Ryan M. Kelly, Vassilis Kostakos
2019 Proceedings of the ACM on Human-Computer Interaction  
Identifying fair predictors is an essential step in the construction of equitable algorithms, but the lack of ground-truth in fair predictor selection makes this a challenging task.  ...  In our study, we recruit 90 crowdworkers to judge the inclusion of various predictors for recidivism. We divide participants across three conditions with varying group composition.  ...  For example, P104 described his consideration on including juvenile charges in the prediction of recidivism, citing both ethical and technical concerns.  ... 
doi:10.1145/3359130 fatcat:roltgi55prdwxd6kdy5jbtjxqi

Young and Dangerous: The Role of Youth in Risk Assessment Instruments

Ingrid Yin
2021 Michigan law review  
Not only is youth undoubtedly the most powerful risk factor in most RAIs, but youth also holds a special place in the criminal justice system as a "mitigating factor of great weight."  ...  This Comment presents the first in-depth critique of RAIs with respect to their treatment of youth.  ...  . 41 The COMPAS algorithm uses the results of a 137-item questionnaire to evaluate an individual's risk of recidivism. 42 Once calculated, an individual's risk is assigned a risk score between one and  ... 
doi:10.36644/mlr.120.3.young fatcat:acdaukpgqzbxfaldvib6oogwhm

The age of secrecy and unfairness in recidivism prediction [article]

Cynthia Rudin, Caroline Wang, Beau Coker
2019 arXiv   pre-print
By partially reverse engineering the COMPAS algorithm -- a recidivism-risk scoring algorithm used throughout the criminal justice system -- we show that it does not seem to depend linearly on the defendant's  ...  In our current society, secret algorithms make important decisions about individuals. There has been substantial discussion about whether these algorithms are unfair to groups of individuals.  ...  If COMPAS depended more heavily on survey questions than on criminal history, it could lead precisely to a kind of bias that we might want to avoid.  ... 
arXiv:1811.00731v2 fatcat:2hrgyaryerea7kwnaq4qe24m6e

Missing the missing values: The ugly duckling of fairness in machine learning

Martínez‐Plumed Fernando, Ferri Cèsar, Nieves David, Hernández‐Orallo José
2021 International Journal of Intelligent Systems  
In this paper, we present the first comprehensive analysis of the relation between missing values and algorithmic fairness for machine learning: (1) we analyse the sources of missing data and bias, mapping  ...  techniques and libraries for handling algorithmic bias get rid of at the first occasion, (3) we study This is an open access article under the terms of the Creative Commons Attribution License, which  ...  We thank the members of the DMIP group of the VRAIN institute for insightful discussions.  ... 
doi:10.1002/int.22415 fatcat:tztr3pmkkrbyzdeziifxpgkgcq

Assessing Risk Among Correctional Community Probation Populations: Predicting Reoffense With Mobile Neurocognitive Assessment Software

Gabe Haarsma, Sasha Davenport, Devonte C. White, Pablo A. Ormachea, Erin Sheena, David M. Eagleman
2020 Frontiers in Psychology  
This novel approach diverges from traditional self-reporting, interview-based, and criminal-records-based approaches, and can also add a protective layer against bias, while strengthening model accuracy  ...  Results gave a recidivism prediction value of 0.70, making it comparable to commonly used risk assessments.  ...  At the end of the pipeline, we're interested in exploring testing re-entry programs, parole supervision, and explore juvenile justice pipelines.  ... 
doi:10.3389/fpsyg.2019.02926 pmid:32038355 pmcid:PMC6992536 fatcat:ddeotlqennfmnkmyvbsyei3nuq

The Impact of Teen Courts on Youth Outcomes: A Systematic Review

Lauren N. Gase, Taylor Schooley, Amelia DeFosset, Michael A. Stoll, Tony Kuo
2015 Adolescent Research Review  
Processing juvenile offenders in the traditional justice system can lead to a range of negative consequences.  ...  Most studies provided little detail regarding the structure or approach of Teen Courts under study and varied widely in research design, comparison group, and operationalization of recidivism, making it  ...  Funding This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.  ... 
doi:10.1007/s40894-015-0012-x fatcat:bujxzoe7jngvnitkvb6owfx5ee

EFFECTIVENESS OF JAPANESE CORRECTIONAL TREATMENTS FOR JUVENILES

Yoshikazu Yuma, Yuichiro Kanazawa, Masaya Kuniyoshi
2006 Behaviormetrika  
In this article we study effectiveness of training school programs relative to probation on recidivism for Japanese juvenile delinquents with differing criminal experiencesearly versus late involvement  ...  We measure effectiveness by the times elapsed from release to reincarceration in the Juvenile Classification Homes.  ...  Juvenile Law (The Juvenile Law; Law No.168 of 1948) emphasis on delinquents' rehabilitation, but there is little research to evaluate these programs' effectiveness in terms of preventing their recidivism  ... 
doi:10.2333/bhmk.33.149 fatcat:ut2z7tdxvffz7lz46y5cargcea
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