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Posted: January 19th, 2023
AI in Sentencing: Investigate how artificial intelligence in sentencing decisions may affect disparities in criminal justice outcomes.
AI in Sentencing: A Critical Analysis
Artificial intelligence (AI) is increasingly used in various domains of society, including criminal justice. One of the applications of AI in this field is to assist judges in making sentencing decisions, based on data and algorithms that predict the risk of recidivism, rehabilitation, and other factors. However, this practice raises several ethical and legal concerns, especially regarding the potential impact of AI on disparities in criminal justice outcomes. This paper aims to investigate how AI in sentencing may affect the fairness and equality of the justice system, and what are the possible solutions to address these challenges.
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The paper is organized as follows. Section 1 provides an overview of the current use and development of AI in sentencing, and the main benefits and drawbacks of this technology. Section 2 examines the sources and consequences of bias and discrimination in AI in sentencing, and how they may affect different groups of offenders and victims. Section 3 discusses the legal and ethical implications of AI in sentencing, and the existing frameworks and principles to regulate and oversee this practice. Section 4 concludes with some recommendations and suggestions for future research.
Section 1: AI in Sentencing: An Overview
AI in sentencing refers to the use of data-driven tools and methods to assist or automate judicial decision-making regarding the type and severity of punishment for convicted offenders. These tools may include risk assessment instruments, sentencing guidelines, decision support systems, or predictive analytics. The main purpose of AI in sentencing is to enhance the efficiency, consistency, transparency, and accuracy of judicial decisions, and to reduce human errors, biases, and subjectivity (Berk et al., 2018).
AI in sentencing is not a new phenomenon, as some forms of data-based decision-making have been used for decades in criminal justice. For example, actuarial risk assessment instruments have been widely adopted to estimate the likelihood of reoffending or violence among offenders, based on statistical models derived from historical data on various risk factors (Monahan & Skeem, 2016). These instruments are used to inform pretrial release, bail, probation, parole, diversion, or treatment decisions. However, the recent advances in AI technology, such as machine learning, natural language processing, or computer vision, have enabled more sophisticated and complex forms of data analysis and prediction, which can incorporate large amounts of structured and unstructured data from various sources, such as court records, police reports, social media, or biometric data (Eisen et al., 2019).
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One of the most prominent examples of AI in sentencing is the use of COMPAS (Correctional Offender Management Profiling for Alternative Sanctions), a proprietary risk assessment tool developed by Northpointe Inc., which has been used by several courts in the US to inform sentencing decisions. COMPAS generates scores for general recidivism risk, violent recidivism risk, pretrial risk, and needs assessment, based on a questionnaire that collects information on various domains such as criminal history, family background, education, employment, substance abuse,
mental health, social networks, or attitudes (Brennan et al., 2009). These scores are intended to guide judges in determining the appropriate sentence for each offender, taking into account their risk level and rehabilitation needs.
Another example of AI in sentencing is the use of PSVM (Public Safety Assessment-Court), a risk assessment tool developed by the Laura and John Arnold Foundation (LJAF), which has been implemented in several jurisdictions across the US as part of a pretrial reform initiative. PSVM uses nine factors derived from administrative data to predict the likelihood of failure to appear, new criminal activity, or new violent criminal activity among defendants awaiting trial. These factors include age at current arrest,
current violent offense charge,
pending charge at time of offense,
prior misdemeanor conviction,
prior felony conviction,
prior violent conviction,
prior failure to appear pretrial in past two years,
prior failure to appear pretrial older than two years,
and prior sentence to incarceration (LJAF write my phd dissertation,
2016). Based on these factors,
PSVM generates two scores: one for failure to appear risk,
and one for new criminal activity risk.
These scores are translated into color-coded categories that indicate the level of supervision or detention recommended for each defendant.
The use of AI in sentencing has been praised by some scholars and practitioners as a way to improve the quality and fairness of judicial decisions,
by providing more objective
and evidence-based information,
by reducing unwarranted disparities
and arbitrariness among judges,
by enhancing public safety
and cost-effectiveness,
and by facilitating individualized justice
and tailored interventions
(Berk et al.,
2018;
Dressel & Farid,
2018;
Starr,
2014).
However,
the use of AI in sentencing has also been criticized by others as a source of potential harm
and injustice,
by introducing new forms of bias
and discrimination,
by violating human rights
and due process principles,
by undermining judicial discretion
and accountability,
and by exacerbating existing inequalities
and power imbalances
in the justice system
(Angwin et al.,
2016;
Chouldechova,
2017;
Eubanks,
2018;
Harcourt,
2015).
These concerns will be discussed in more detail in the following sections.
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