Justice Systems Utilizing Artificial Intelligence for Risk Assessment
Justice systems across the globe are utilizing artificial intelligence (AI) technology to evaluate individuals with criminal convictions. These AI systems, powered by machine learning algorithms, are primarily designed to predict the likelihood of reoffending. They play a significant role in the decision-making processes of courts, prisons, parole boards, and probation officers.
The United Kingdom’s Implementation of AI Technology
The United Kingdom has been implementing this technology since 2001 when the Offender Assessment System (Oasys) was introduced. Oasys replaced certain responsibilities of probation officers with a risk assessment tool. However, for over two decades, independent scientists have not been granted access to the data behind Oasys, hindering the opportunity for unbiased analysis and assessment of its accuracy.
Lack of transparency is a common issue with AI systems. Their complex decision-making processes often remain obscure without advanced technical knowledge, resembling black boxes. Supporters argue that AI algorithms are more objective than human assessments since they are standardized, reducing potential biases. Critics, however, raise concerns about the limited access to data, which calls into question the presence of biases within a system that utilizes data from criminal justice institutions known for their skewed treatment of ethnic minorities.
The Ministry of Justice argues that external evaluation poses data protection concerns as it would require access to personal information, including protected characteristics such as race, ethnicity, and gender. Discrimination based on these protected characteristics is illegal.
The Impact of Oasys on the Justice System
Since its introduction, Oasys has drastically transformed how courts and probation services evaluate individuals convicted of crimes in the UK. Algorithms now play a substantial role in assessing the risk posed by individuals involved in the justice system, including defendants awaiting punishment, prisoners, and parole applicants. The traditional client-based casework approach, involving interviews with probation officers, has been significantly reduced, replaced by algorithmic predictions.
Machine learning predictions inform various decisions, including granting bail, determining sentences (community-based, custodial, or suspended), assigning prison security classifications, and recommending rehabilitation programs. They also influence the supervision conditions for individuals convicted of crimes in the community and the possibility of early release from prison.
Before Oasys, early attempts at risk assessment were relatively crude, utilizing only a handful of predictors and informal statistical methods. However, with the advancements in computer technology, the UK Home Office recognized the potential of predictive algorithms to effectively allocate scarce resources, protect the public, and identify individuals at high risk of reoffending.
The Home Office commissioned the Offender Group Reconviction Scale (OGRS) in 1996, which used statistical methods to predict the risk of reoffending based on a person’s past criminal history. OGRS is still in use today and has been incorporated into Oasys, expanded to include additional machine learning algorithms that predict different types of reoffending, with reconviction within two years of release as the primary measure.
Oasys operates based on the “what works” approach to risk assessment, which relies on objective evidence of effective reoffending reduction strategies. This approach gained popularity worldwide in the 1990s, introducing fundamental principles of risk assessment and rehabilitation.
The Need for Transparency and Independent Evaluation
As AI technology continues to shape justice systems, the need for transparency, accountability, and independent evaluation remains crucial. Access to data and comprehensive information is vital to address concerns regarding biases and ensure fair and accurate assessments of reoffending risk.
SDGs, Targets, and Indicators in the Article
1. Which SDGs are addressed or connected to the issues highlighted in the article?
- SDG 16: Peace, Justice, and Strong Institutions
The article discusses the use of artificial intelligence (AI) technology in justice systems to evaluate individuals with criminal convictions. This topic is directly connected to SDG 16, which aims to promote peaceful and inclusive societies, provide access to justice for all, and build effective, accountable, and inclusive institutions at all levels.
2. What specific targets under those SDGs can be identified based on the article’s content?
- Target 16.3: Promote the rule of law at the national and international levels and ensure equal access to justice for all.
- Target 16.7: Ensure responsive, inclusive, participatory, and representative decision-making at all levels.
The use of AI technology in justice systems raises questions about equal access to justice and the decision-making processes involved. Therefore, targets 16.3 and 16.7 are relevant to the issues discussed in the article.
3. Are there any indicators mentioned or implied in the article that can be used to measure progress towards the identified targets?
- Indicator 16.3.1: Proportion of victims of violence in the previous 12 months who reported their victimization to competent authorities or other officially recognized mechanisms.
- Indicator 16.7.1: Proportions of positions in public institutions (national and local legislatures, public service, and judiciary) compared to national distributions, by sex, age, persons with disabilities, and population groups.
The article does not explicitly mention any indicators, but based on the identified targets, the following indicators can be used to measure progress towards those targets. Indicator 16.3.1 measures the proportion of victims of violence who report their victimization, which relates to equal access to justice. Indicator 16.7.1 measures the representation of different population groups in public institutions, which is relevant to inclusive and participatory decision-making.
Table: SDGs, Targets, and Indicators
| SDGs | Targets | Indicators |
|---|---|---|
| SDG 16: Peace, Justice, and Strong Institutions | Target 16.3: Promote the rule of law at the national and international levels and ensure equal access to justice for all. | Indicator 16.3.1: Proportion of victims of violence in the previous 12 months who reported their victimization to competent authorities or other officially recognized mechanisms. |
| SDG 16: Peace, Justice, and Strong Institutions | Target 16.7: Ensure responsive, inclusive, participatory, and representative decision-making at all levels. | Indicator 16.7.1: Proportions of positions in public institutions (national and local legislatures, public service, and judiciary) compared to national distributions, by sex, age, persons with disabilities, and population groups. |
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Source: fagenwasanni.com
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