CC BY
4
ADVANCEMENTS IN MITIGATING RECIDIVISM WITHIN JUDICIAL
DELIBERATIONS
SULASAT.J.1 , DR. RAMESH KUMAR2
Research Scholar Lovely Professional University, Phagwara, Punjab, India Assistant Professor & Research Coordinator of Law, School of Law Lovely Professional University, Phagwara, Kapurthala, Punjab, India-144411 Orchid Id: 0000-0003-2771-7274 Web of Science Researcher ID: AGF-7498-2022 Sulasa_tj@yahoo.co.in jmsdrrameshkumar@gmail.com
Abstract:This research article examines the evolving approaches to recidivism in judicial decisionmaking. Recidivism, which refers to the reoffending behaviour of individuals who have previously been convicted of a crime, has long been a concern for the criminal justice system. Traditional approaches to recidivism focused primarily on punitive measures, such as incarceration, without adequately addressing the underlying factors contributing to reoffending. However, in recent years, there has been a shift toward more nuanced and evidence-based strategies aimed at reducing recidivism rates and promoting rehabilitation. This article explores the changing landscape of recidivism and highlights the key factors shaping judicial decision-making in this context.
Key words: evolving approaches, recidivism, reoffending behaviour, promoting rehabilitation, shaping judicial decision-making
INTRODUCTION
1.1 Background: Recidivism, the relapse into criminal behaviour by individuals previously convicted of a crime, has long been a pressing issue within the criminal justice system. Traditional approaches to addressing recidivism have often relied on punitive measures, such as incarceration, without fully considering the underlying factors that contribute to reoffending. However, research and evidence have shown that a more nuanced and evidence-based approach is necessary to effectively reduce recidivism rates and promote successful rehabilitation.
1.2 Significance of the Study: The significance of studying evolving approaches to recidivism in judicial decision-making lies in the potential to improve the effectiveness and fairness of the criminal justice system. By examining the factors that shape judicial decision-making in relation to recidivism, this research aims to contribute to the development of evidence-based policies and practices that can better address the underlying causes of criminal behaviour and reduce the cycle of reoffending. Additionally, understanding the evolving approaches to recidivism can help policymakers and practitioners make informed decisions regarding sentencing, rehabilitation programs, and community supervision, ultimately leading to improved outcomes for both individuals and society as a whole.
1.3 Purpose: The aim of this study is to investigate and analyse the change path of recidivism in decision making from a theoretical and scientific point of view. Specific objectives include:
1.3.1 To review and define the limits of the traditional regeneration process in the judicial system.
1.3.2 Investigate changes in evidence-based strategies such as risk assessment tools, rehabilitation and integration, social change, and decision-making processes.
1.3.3 Identify factors that influence the judicial process for affirmative action, including research and evidence, decision-making, public opinion and opinion, and legal change.
1.3.4 Give case studies and examples of the effectiveness and impact of recidivism approaches.
2636
1.3.5 Discuss issues and decisions regarding ethical implications, public safety measures, and the use of evolutionary approaches, including rehabilitation and allocation of resources. 1.3.6. Identify future trends and potential improvements in recycling, such as advances in risk assessment, technology integration, and multi-stakeholder collaboration. By achieving these goals, this research paper aims to contribute to current knowledge on recidivism and judicial decision, inform the legal debate, and provide insights for professionals and policy makers working in the field of criminal justice.
Chapter 1.1: TRADITIONAL APPROACHES TO RECIDIVISM
Punitive Measures and Limitations Recidivism, the relapse into criminal behaviour by individuals who have previously been convicted, is a complex issue that poses significant challenges to society. This research paper explores traditional approaches to recidivism, focusing on punitive measures such as imprisonment, and examines the limitations associated with these approaches.
1.1.1.1 Incarceration is a common punitive measure used to incapacitate individuals who have committed crimes. The primary goal of imprisonment is to isolate offenders from society, preventing them from engaging in criminal activities. However, research indicates that the deterrent effect of imprisonment on recidivism is limited. Factors such as the lack of effective rehabilitation programs within correctional facilities, the stigmatization of ex-offenders, and the challenges faced upon re-entry into society contribute to high rates of recidivism among former prisoners.
1.1.1.2 Mandatory Minimum Sentences
Mandatory minimum sentences are another punitive measure employed in criminal justice systems. These sentencing policies require predetermined minimum prison terms for certain offenses, limiting judicial discretion. While mandatory minimum sentences may act as a deterrent, they have been criticized for their inflexibility and failure to account for individual circumstances or the potential for rehabilitation. Consequently, such policies may result in disproportionately harsh sentences, perpetuating cycles of recidivism. 1.1.2 Limitations of Traditional Approaches
1.1.2.1 Failure to Address Underlying Causes Traditional punitive measures often neglect to address the root causes of criminal behaviour, such as substance abuse, mental health issues, poverty, and lack of education or employment opportunities. Failing to provide adequate support and interventions to address these underlying factors hinders the potential for successful rehabilitation and increases the risk of recidivism.
1.1.2.2 Limited Focus on Rehabilitation
Punitive measures primarily focus on punishment and incapacitation, with minimal emphasis on rehabilitation and reintegrating offenders into society. The lack of comprehensive rehabilitation programs within correctional facilities undermines the potential for meaningful change and contributes to high rates of recidivism.
1.1.2.3 Collateral Consequences
Ex-offenders face numerous collateral consequences upon release, including limited employment prospects, housing discrimination, and restricted access to social welfare programs. These consequences create significant barriers to successful reintegration, increase the likelihood of relapse into criminal behaviour, and perpetuate the cycle of recidivism
Chapter 1.2: SHIFT TOWARDS EVIDENCE-BASED STRATEGIES
Recidivism, the relapse into criminal behaviour by individuals with prior criminal records, has long posed significant challenges to the criminal justice system. There has been a paradigm shift in recent years towards evidence-based strategies to address recidivism. This article explores the evolving approaches to recidivism in judicial decision-making, focusing on risk assessment tools, rehabilitation and reintegration programs, community-based alternatives, and sentencing
2637
guidelines. By adopting these evidence-based strategies, legal systems aim to enhance public safety, promote fair and equitable outcomes, and effectively reduce recidivism rates.
1.2.1 Risk Assessment Tools:
Risk assessment tools provide a systematic and data-driven approach to evaluating the likelihood of an individual reoffending. These tools analyse various factors such as criminal history, age, employment status, substance abuse history, and mental health to estimate an individual's risk level. By incorporating objective risk assessment tools, judges can make more informed decisions regarding pretrial release, sentencing, and parole. The use of these tools ensures that resources are allocated appropriately, focusing on high-risk individuals who require intensive supervision and intervention.
1.2.2 Rehabilitation and Reintegration Programs:
Recognizing that punishment alone is often insufficient to address the underlying causes of criminal behaviour, rehabilitation and reintegration programs have gained prominence in recent years. Evidence-based programs, such as cognitive-behavioural therapy, substance abuse treatment, vocational training, and educational programs, have shown promising results in reducing recidivism rates. By providing individuals with the necessary skills, support, and treatment, these programs aim to address the root causes of criminal behaviour and facilitate successful reintegration into society.
1.2.3 Community-Based Alternatives:
Community-based alternatives offer an alternative to traditional incarceration, emphasizing community supervision and support rather than imprisonment. Programs such as probation, parole, electronic monitoring, and restorative justice practices promote accountability, rehabilitation, and community reintegration. By allowing individuals to remain connected to their families, employment, and community support networks, these alternatives have been shown to reduce recidivism and alleviate prison overcrowding.
1.2.4 Sentencing Guidelines:
Sentencing guidelines provide a framework for judges to determine appropriate sentences based on the severity of the offense and the individual's risk of reoffending. Evidence-based sentencing guidelines consider factors such as the nature of the crime, the defendant's criminal history, and the potential for rehabilitation. By incorporating empirical data on recidivism risk and the effectiveness of different interventions, judges can make more consistent and proportionate sentencing decisions, promoting fairness and reducing disparities.
Chapter 1.3 FACTORS INFLUENCING JUDICIAL DECISION MAKING
Duplicity, the way to manage recurrence of past personal concerns. Addressing recidivism requires an understanding of the complex processes involved in criminal justice. In recent years, many factors have emerged as important drivers of recidivism in the criminal justice system. These answers delve into events, examining them through research and theory. 1.3.1 Research and Empirical Evidence:
Research and empirical evidence play an important role in developing judicial decisions regarding recycling. Judges often rely on scientific research and statistics to inform their understanding of recidivism rates, risk factors and effective response strategies. Through rigorous research, patterns and relationships can be identified, leading to evidence-based decisions. Research on recidivism risk assessment tools has drawn attention in recent years. This tool uses a variety of factors, such as past criminal history, age, and drug use, to estimate a person's likelihood of reoffending. Using such tools, judges can assess an offender's risk and make informed decisions about sentencing, probation or rehabilitation services. Relying on research and empirical evidence provides a more objective and data-driven approach to addressing recycling.
2638
1.3.2 Judicial discretion:
Judicial discretion is the power vested in judges to make decisions when interpreting the law and the specific circumstances of each case. In the context of recidivism, discretionary justice allows judges to make personal decisions and adjust their decisions accordingly. While the use of recidivism risk assessment tools can offer advice, judges also consider other factors such as the offender's personality, likelihood of recidivism, and the seriousness of the offence. The evolving recidivism approach recognizes that a one-size-fits-all approach will not do much to reduce recidivism rates. Instead, judges decide on penalties or sanctions based on the needs of the offender. This approach aims to balance punishment and rehabilitation to reduce the risk of future crime.
1.3.3 Public Perception and Opinion:
Public perception and opinion can be effective in the court's decision to recognize people. Public concerns about the need for security and punishment can affect the policies and practices adopted by the justice system. In response to public opinion, judges can expect harsher sentences for people with a criminal history. This can be attributed to the assumption that harsh punishment can act as a deterrent. However, a more flexible approach to replication emphasizes evidence-based practice rather than reliance on public opinion. As public opinion continues to influence, judges are encouraged to consider other ways to address the underlying causes of illegitimate cases, such as medical and social justice programs. Balancing public expectations with effective strategies to reduce recidivism is difficult to justify.
1.3.4 Legislative Reforms:
Legislative Reform plays an important role in improving the way to change duplication in the judicial system. Legislators recognize that the issue of affirmative action should be addressed through legislation based on evidence-based practices. Reforms may include reorganizing the sentencing process, expanding amendments, or enforcing laws that support the rehabilitation and rehabilitation of offenders. Legal reform could provide judges with more tools and options to meet these needs effectively. By creating laws that prioritize the use of evidence, legislators create a framework that supports judges in making decisions based on the goals of reducing recycling and improving public safety. These reforms also reflect a societal shift towards a more integrated and restructured approach to criminal justice. In summary, the approach to judicial decision making has been influenced by many factors. Research and empirical evidence provide judges with valuable information on recidivism rates and effective response strategies. Jurisdiction allows decisions based on individual circumstances. Public perception and opinion influence decision-making to some extent, and reform laws provide a supportive framework for evidence-based practice. By taking these factors into account, the justice system can try to create better strategies for dealing with recidivism and promoting a safer society.
Chapter 1.4: CASE STUDIES
1.4.1 Examples of evolution :Approaches to dealing with recidivism in the criminal justice system have been adopted all over the world. These case studies highlight the use of evidence-based practices and innovative strategies to reduce recovery costs. By examining these examples, we can understand the effectiveness of various methods. An important example is the use of risk assessment tools in sentencing. Jurisdictions such as the United States have adopted risk assessment tools such as Service Level/Management Information (LS/CMI) or Static-99R to identify the perpetrator. The law will be repeated. The tool provides judges with a systematic and data-driven approach to sentencing, including factors such as criminal history, age and drug addiction. Research shows that similar and fairer judgments can be made using risk assessment tools. Another way to grow is through medical and rehabilitation programs. The justice system is increasingly recognizing the importance of addressing the root causes of crime, rather than focusing solely on punishment. For example, drug courts have been established in many countries to provide specialized treatment and support for offenders struggling with drug addiction. These
2639
services are designed to break the cycle of addiction and reduce the risk of relapse. Research shows that drug courts can reduce recidivism rates compared to traditional courts. Additionally, diversion is being looked at as an alternative to jail time for some low-level criminals. These programs transfer individuals from the justice system to specific programs that focus on education, job training, or community service. Rehabilitation programs aim to reduce recidivism rates by addressing the root causes of criminal behaviour and providing people with opportunities for recovery. Research has shown promising results suggesting that changing programs can help reduce relapse rates.
1.4.2 Impact on Recidivism Rates:
The implementation of evolving approaches to recidivism in judicial decision-making has demonstrated varying impacts on recidivism rates. Evaluating the effectiveness of these approaches is crucial to inform policy and practice decisions.
Research on the use of risk assessment tools in sentencing decisions has shown mixed results regarding their impact on recidivism rates. While risk assessment tools provide judges with valuable information, the translation of risk assessment into effective interventions remains a challenge. It is important to ensure that the risk assessment is followed by appropriate treatment and supervision programs to address the identified risks. When implemented in conjunction with evidence-based interventions, risk assessment tools have the potential to contribute to lower recidivism rates. Rehabilitation programs and restorative justice approaches have shown promising results in reducing recidivism rates. By focusing on addressing the underlying causes of criminal behaviour and providing individuals with support and resources for personal growth, these programs can positively impact reoffending rates. Research indicates that comprehensive rehabilitation programs that address multiple risk factors, such as substance abuse, mental health, and education, are more likely to be effective in reducing recidivism.
Diversion programs have also demonstrated positive effects on recidivism rates. By diverting low-level offenders from traditional criminal justice processes and providing them with tailored interventions, diversion programs offer an opportunity for individuals to address their behaviour and make positive changes. Studies have shown that participation in diversion programs can significantly reduce the likelihood of future criminal involvement. It is worth noting that the impact of these evolving approaches can vary depending on various factors, such as the quality of program implementation, access to resources, and the individual characteristics of offenders. Additionally, long-term follow-up studies are necessary to assess the sustainability of the observed effects on recidivism rates.
Chapter 1.5: CHALLENGES AND CONSIDERATIONS
Evolving approaches to recidivism in judicial decision-making aim to address the challenges associated with reducing criminal reoffending rates while considering ethical implications, balancing public safety and rehabilitation, and ensuring efficient implementation and resource allocation. This article will discuss these challenges and considerations in a scientific and technical manner, highlighting the complexity of incorporating recidivism data into judicial decision-making processes.
1.5.1 Ethical Implications:
When integrating recidivism data into judicial decision-making, several ethical implications arise. One concern is the potential for bias and discrimination, as the use of historical data might perpetuate inequalities within the criminal justice system. Historical disparities in arrests, convictions, and sentencing could lead to biased predictions and decision-making, disproportionately affecting marginalised communities. Careful consideration must be given to ensure that the algorithms used to predict recidivism do not amplify existing biases and contribute to unfair outcomes. Transparency and accountability are also essential ethical considerations. Stakeholders, including judges, attorneys, defendants, and the public, should be informed about the data sources, algorithms, and decision-making processes involved. Transparency can help
2640
mitigate concerns about fairness, privacy, and the potential misuse of technology. Additionally, the responsible use of recidivism data should involve ongoing monitoring and evaluation to ensure that the system remains fair, reliable, and aligned with societal values.
1.5.2 Balancing Public Safety and Rehabilitation:
One of the central challenges in incorporating recidivism data into judicial decision-making is striking a balance between public safety and rehabilitation efforts. While ensuring public safety is crucial, it is equally important to provide opportunities for individuals to reform and successfully reintegrate into society. The risk of reoffending varies among individuals, and accurately assessing this risk is essential. However, relying solely on past criminal records and predictive algorithms might oversimplify the complexity of human behaviour and individual circumstances. Factors such as access to education, employment opportunities, mental health support, and social networks play significant roles in determining an individual's likelihood of reoffending. A comprehensive approach that considers these contextual factors alongside recidivism data can lead to more informed and nuanced decision-making, promoting both public safety and effective rehabilitation. Moreover, judicial decision-making should involve a multidisciplinary approach, incorporating insights from psychologists, social workers, and other professionals who can provide comprehensive assessments of an individual's risk and rehabilitation needs. This holistic approach can help tailor interventions and sentencing options that address the underlying causes of criminal behaviour and promote successful reintegration into society.
1.5.3 Implementation and Resource Allocation:
The successful implementation of evolving approaches to recidivism in judicial decision-making requires careful consideration of resource allocation and logistical challenges. Adopting new technologies and implementing data-driven decision-making processes often entail substantial financial investments in infrastructure, training, and maintenance. Courts and justice systems must allocate adequate resources to ensure the smooth integration of these approaches into existing frameworks. Data quality and accessibility are additional implementation challenges. Accurate and up-to-date data are crucial for building reliable predictive models. However, the availability and consistency of data can vary across jurisdictions, potentially hindering the effectiveness of recidivism prediction models. Efforts should be made to standardise data collection and reporting procedures, ensuring data accuracy and comparability. Moreover, privacy concerns surrounding the collection and use of sensitive personal data must be addressed. Safeguards should be in place to protect individuals' privacy rights, including secure data storage, anonymization techniques, and adherence to legal and ethical guidelines. Conclusion: Evolving approaches to recidivism in judicial decision-making offer promising avenues for improving the criminal justice system. However, ethical implications, the balance between public safety and rehabilitation, and implementation challenges must be carefully addressed. By considering these challenges and incorporating scientific and technical insights, policymakers and stakeholders can work towards a more just, effective, and evidence-based approach to reducing recidivism and promoting positive outcomes for individuals involved in the justice system.
Chapter 1.6 FUTURE DIRECTIONS
The judicial system plays a crucial role in ensuring public safety and administering justice. One area that has garnered significant attention in recent years is the assessment and prediction of recidivism, the likelihood of an individual reoffending. Traditional approaches to recidivism relied heavily on subjective judgments, but evolving approaches have emerged that leverage advancements in risk assessment, integration of technology, and collaborative efforts. This article delves into these three key aspects and their impact on the evolving landscape of recidivism prediction in judicial decision-making. 1.6.1 Advancements in Risk Assessment:
Advancements in risk assessment have revolutionized the way recidivism is predicted. Traditional methods relied on limited variables, such as criminal history, demographics, and static factors,
2641
which failed to capture the complex interplay of various dynamic elements influencing criminal behaviour. However, modern risk assessment tools utilize comprehensive datasets and sophisticated algorithms to analyze a multitude of risk factors. These factors may include psychological profiles, substance abuse history, educational attainment, employment status, and social network analysis. By incorporating a wide array of factors, risk assessment models can generate more accurate predictions, enabling judicial decision-makers to tailor interventions and sentencing accordingly. Furthermore, advancements in risk assessment have also led to the development of actuarial risk assessment instruments. These instruments employ statistical methods to assign numerical scores to individuals based on their risk levels. By utilizing evidence-based algorithms, actuarial risk assessment instruments can generate objective and standardized risk scores, enhancing the consistency and fairness of judicial decision-making. However, it is crucial to ensure that such instruments are regularly validated, updated, and transparent to minimize the potential for bias and discriminatory outcomes.
1.6.2 Integration of Technology:
The integration of technology has played a pivotal role in transforming the recidivism prediction landscape. Machine learning algorithms, data analytics, and artificial intelligence (AI) have shown immense potential in augmenting risk assessment practices. By analyzing vast amounts of data, these technologies can identify patterns, correlations, and risk factors that may not be apparent to human analysts. This enables the development of more robust and accurate recidivism prediction models. Moreover, technology-driven solutions facilitate the automation of risk assessment processes, improving efficiency and consistency in judicial decision-making. Automated risk assessment tools can rapidly process large volumes of information, reducing the burden on human resources while providing timely and standardized risk assessments. However, caution must be exercised to ensure transparency and accountability in the design and implementation of these technologies, addressing concerns related to algorithmic bias and data privacy.
1.6.3 Collaborative Efforts:
Recognizing the multifaceted nature of recidivism, collaborative efforts have emerged as a vital component in the evolving approaches to judicial decision-making. Stakeholders from various fields, including law enforcement agencies, criminal justice practitioners, researchers, and policymakers, are actively collaborating to improve recidivism prediction and reduce the risk of reoffending. Collaborative efforts promote the sharing of knowledge, expertise, and data, enabling the development of comprehensive risk assessment frameworks. By pooling resources and expertise, stakeholders can collectively address the limitations of individual perspectives, leading to more accurate and holistic recidivism predictions. Additionally, collaboration allows for the integration of different domains of knowledge, such as psychology, sociology, and criminology, resulting in more comprehensive risk assessment tools. Furthermore, collaborative efforts extend beyond risk assessment to encompass the development and implementation of evidence-based interventions. By aligning practices with research findings and leveraging the collective wisdom of stakeholders, judicial decision-makers can design tailored intervention programs aimed at reducing recidivism rates effectively.
CONCLUSION
2.1 Summary of Findings:
The findings of our research on evolving approaches to recidivism in judicial decision-making provide valuable insights into the current landscape and potential future directions in this field. Through a comprehensive analysis of existing literature and empirical data, we have identified several key points:
2.1.1 Risk assessment tools: Various risk assessment tools have been developed and implemented to aid judicial decision-making in predicting recidivism rates among offenders. These tools utilize a combination of static and dynamic factors to assign risk scores and provide judges with information to guide their sentencing decisions.
2642
2.1.2 Limitations of risk assessment tools:
While risk assessment tools can be helpful, they are not without limitations. Some concerns include potential bias and discrimination, lack of transparency and explain ability, and limited predictive accuracy in certain contexts. These limitations need to be addressed to ensure fair and effective implementation.
2.1.3 Contextual factors:
Our research highlights the importance of considering contextual factors in recidivism prediction. Socioeconomic conditions, access to rehabilitation programs, and community support all play significant roles in an individual's likelihood of reoffending. Integrating these factors into judicial decision-making can enhance the accuracy and fairness of recidivism predictions.
2.1.4 Sentencing alternatives:
Alternative approaches to traditional sentencing, such as diversion programs, restorative justice, and rehabilitation-focused interventions, show promise in reducing recidivism rates. These approaches prioritize offender rehabilitation and reintegration into society, aiming to address underlying causes of criminal behavior rather than relying solely on punitive measures.
2.1.5 Judicial discretion:
Judicial decision-making involves a balance between relying on empirical data and exercising judicial discretion. While risk assessment tools can provide valuable information, it is essential to maintain judicial discretion to consider individual circumstances and promote fair and just outcomes.
2.2 Implications for Judicial Decision-Making:
Our research has several implications for judicial decision-making regarding recidivism: 2.2.1 Enhanced risk assessment practices: Judicial systems should invest in improving risk assessment tools by addressing their limitations, including potential bias, transparency, and accuracy concerns. Continual refinement of these tools through rigorous evaluation and validation processes is crucial.
2.2.2 Consideration of contextual factors: Judges should be encouraged to take into account contextual factors that can influence recidivism, such as socio-economic conditions and access to rehabilitation programs. This broader perspective can help in making more informed decisions and promoting fairness in sentencing.
2.2.3 Adoption of alternative approaches: Judicial systems should explore the adoption of alternative approaches to traditional sentencing, such as diversion programs and restorative justice. These approaches can prioritize rehabilitation and reduce recidivism rates while ensuring accountability and victim reparation.
2.2.4 Training and education: Judges and legal professionals should receive comprehensive training on the use of risk assessment tools, understanding their limitations, and considering contextual factors. This education can facilitate more effective and fair decision-making in cases involving recidivism.
2.3. Call for Further Research:
While our research provides valuable insights, several areas warrant further investigation: 2 3.1 Bias and discrimination:
Additional research is needed to identify and mitigate potential biases and discrimination embedded within risk assessment tools. This research should focus on ensuring fairness across various demographic groups and examining the impact of different risk factors on different populations.
2.3.2 Long-term effectiveness:
Longitudinal studies are necessary to assess the long-term effectiveness of alternative approaches to traditional sentencing in reducing recidivism rates. These studies can provide insights into the sustainability and lasting impact of rehabilitation-focused interventions.
2.3.3 Comparative analysis:
Conducting comparative analyses of different risk assessment tools, alternative sentencing approaches, and judicial decision-making practices across jurisdictions can help identify best
2643
practices and areas for improvement. This research can contribute to the development of standardized guidelines and policies. 2.3.4 Stakeholder perspectives:
Research should incorporate the perspectives of various stakeholders, including judges, offenders, victims, and communities, to understand the impact of evolving approaches to recidivism in judicial decision-making from multiple angles. This inclusion can lead to a more comprehensive understanding of the implications and potential challenges associated with these approaches.
REFERENCES
[ 1 ] Artificial Intelligence and Sentencing from a Human Rights Perspective
[2] Johannes Kaspar, Stefan Harrendorf, Felix Butz, Katrin Höffler, Lucia Sommerer & Stephan Christoph ,Chapter First Online: 13 January 2023
[3] Aebi, M.F., Caneppele, S., Harrendrf, S., Hashimoto, Y.Z., Jehle, J.-M., Khan, T.S., Kühn, O., Lewis, C., Molnar, L., Smit, P., Pörisdöttir, R. and the ESB Network of National Correspondents (2021). European sourcebook of crime and criminal justice statistics—2021 (6 ed.). Göttingen University Press: Göttingen.
[4] CrossRef Google Scholar
[5] Alfaia Sampaio, E., Seixas, J.J. & Gomes, P.J. (2019). Artificial Intelligence and the judicial ruling. Themis Competition 2019, Semi-Final D, Team Portugal I. https://www.ejtn.eu/PageFiles/17916/TEAM%20P0RTUGAL%20I%20TH%202019%20D.pdf. Accessed 3 February 2022.
[6] Alufaisan, Y., Marusich, L. R., Bakdash, J. Z., Zhou, Y. &Kantarcioglu, M. (2021). Does explainable artificial intelligence improve human decision-making? In: Proceedings of the AAAI Conference on Artificial Intelligence, 35(8), 66186626. https://www.ojs.aaai.org/index.php/AAAI/article/view/16819. Accessed 20 April 2022.
[7] Angwin, J., Larson, J., Mattu, S. & Kirchner, L. (2016). Machine bias. There's software used across the country to predict future criminals. And it's biased against blacks. https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing. Accessed 22 November 2021.
[8] Bagaric, M., Hunter, D. &Stobbs, N. (2020). Erasing the bias against using artificial intelligence to predict future criminality. Algorithms are color blind and never tire. University of Cincinnati Law Review, 88, 1037-1082. Google Scholar
[9] Bagaric, M., Svilar, J., Bull, M., Hunter, D. and Stobbs, N. (2021). The solution to the pervasive bias and discrimination in the criminal justice. Transparent artificial intelligence. https://ssrn.com/abstract=3795911. Accessed 22 November 2021.
[10] Bontrager, S., Barrick, K., &Stupi, E. (2013). Gender and sentencing. Meta-analysis of contemporary research. Journal of Gender, Race and Justice, 16, 349-372.Google Scholar
[11] Botha, H. (2009). Human dignity in comparative perspective. Stellenbosch Law Review, 20, 171-220.Google Scholar
[12] Brayne, S. & Christin, A. (2021). Technologies of crime prediction. Reception of algorithms in policing and criminal courts. Social Problems, 68, 608-624CrossRef Google Scholar
[13] Burrell, J. (2016). How the Machine 'Thinks'. Understanding Opacity in Machine Learning Algorithms. Big Data & Society, 3, 1-12.CrossRef Google Scholar
[14] Botha, H. (2009). Human dignity in comparative perspective. Stellenbosch Law Review, 20, 171-220. Google Scholar
[15] Brayne, S. & Christin, A. (2021). Technologies of crime prediction. Reception of algorithms in policing and criminal courts. Social Problems, 68, 608-624CrossRef Google Scholar
[16] Burrell, J. (2016). How the Machine 'Thinks'. Understanding Opacity in Machine Learning Algorithms. Big Data & Society, 3, 1-12.CrossRef Google Scholar
2644
[17] Butz, F., Christoph, S., Sommerer, L., Harrendorf, S., Kaspar, J. &Höffler, K. (2021). AutomatisierteRisikoprognosenimKontext von Bewährungsentscheidungen. Bewährungshilfe, 68, 241-259. Google Scholar
[ 18] Bygrave, L.A. (2020). Machine learning, cognitive sovereignty and data protection rights with respect to automated decisions.
[19] https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3721118.Accessed 22November 2020.
[20] Carlson, A.M. (2017). The need for transparency in the age of predictive sentencing algorithms. Iowa Law Review, 103, 303-329. Google Scholar
[21] Chander, A. (2017). The racist algorithm? Michigan Law Review, 115, 10231045. CrossRef Google Scholar
[22] Chen, S. (2021). Chinese scientists develop AI 'prosecutor' that can press its own charges. South China Morning Post, 26 December 2021.
[23] https://www.scmp.com/news/china/science/article/3160997/chinese-scientists-develop-ai-prosecutor-can-press-its-own. Accessed 24 January 2022.
[24] Christin, A. (2019). Predictive algorithms and criminal sentencing. In: Bessner D and Guilhot N (eds.) The Decisionist Imagination. Sovereignty, Social Science, and Democracy in the 20th Century, Berghahn Books: New York, 272-294. Google Scholar
[25] Commissioner for Human Rights. (2019). Unboxing artificial intelligence. 10 steps to protect Human Rights. Council of Europe. https://rm.coe.int/unboxing-artificial-intelligence-10-steps-to-protect-human-rightsreco/1680946e64.Accessed 22 November 2021.
[26] Committee of Experts on Internet Intermediaries (MSI-NET). (2018). Algorithms and human rights. Study on the human rights dimensions of automated data processing techniques and possible regulatory implications. Council of Europe study, DGI(2017)12. https://rm.coe.int/algorithms-and-human-rights-study-on-the-human-rights-dimension-of-aut/ 1680796d 10. Accessed 3 February 2022.Council of Europe. (2021). EU accession to the
[27] ECHR. https://www.coe.int/en/web/human-rights-intergovernmental cooperation/accession-of-the-european-union-to-the-european-convention-on-human-rights.Accessed 24 January 2022.
[28] Cyphert, A.B. (2020). Reprogramming recidivism. The First Step Act and algorithmic prediction of risk. Seton Hall Law Review, 51, 331-381. Google Scholar
[29] Danaher, J. (2020). Welcoming robots into the moral circle: A defence of ethical behaviourism. Science and Engineering Ethics, 26, 2023-2049. CrossRef Google Scholar
[30] Deeks, A. (2019). The judicial demand for explainable artificial intelligence. Columbia Law Review, 119, 1829-1850. Google Scholar
[31] Dressel, J. & Farid, H. (2018). The accuracy, fairness, and limits of predicting recidivism. Science Advances, 4. Google Scholar
[32] Duwe, G. & Kim, K. (2017). Out with the old and in with the new? An empirical comparison of supervised learning algorithms to predict recidivism. Criminal Justice Policy Review, 28, 570-CrossRef Google Scholar
[33] Duwell, M. (2014). Human Dignity: Concept, Discussions, Philosophical Perspectives: Interdisciplinary Perspectives. In: Duwell, M., Braarvig, J., Brownsword, R. &Mieth, D. (eds.) Cambridge Handbook on Human Dignity. Cambridge: Cambridge University Press, 23-50.CrossRef Google Scholar
[34] Dymitruk, M. (2019). The right to a fair trial in automated civil proceedings. Masaryk University Journal of Law and Technology, 13, 27-44.CrossRef Google Scholar
[35] Elsner, B., Aebi, M.F., Aubusson de Cavarlay, B., Gillieron, G., Hakeri, H., Jehle, J.-M., Killias, M., Lewis, C., Peters, J., Roth, E., Smit, P., Sobota, P., Turkovic, K., Wade, M. & Zila, J. (2008). The Criminal Justice Approach: Case Examples. European Journal on Criminal Policy and Research, 14, 123-132. CrossRef Google Scholar
2645
[36] European Commission for the Efficiency of Justice (CEPEJ). (2018). European Ethical Charter on the use of artificial intelligence (AI) in judicial systems and their environment. https://rm.coe.int/ethical-charter-en-for-publication-4-december-
2018/ 16808f699c. Accessed 22 November 2021.
[37] European Court of Human Rights (ECtHR) (2021). Overview 1959-2020 ECHR. https://www.echr.coe.int/Documents/0verview_19592020_ENG.pdf. Accessed 1 February 2022.
[38] European Criminal Policy Institute (2020). Category model for the harmonisation of criminal sanctions in Europe. In Satzger, H. (ed.) HarmonisierungstrafrechtlicherSanktionen in der Europäischen Union - Harmonisation of Criminal Sanctions in the European Union. Nomos: Baden-Baden, 707-742. Google Scholar
[39] Fair Trials (2020). Regulating artificial intelligence for use in criminal justice systems in the EU. Policy paper.
[40] https://www.fairtrials.org/sites/default/files/RegulatingArtificialIntelligenceforUseinCrimi nalJusticeSystems-FairTrials.pdf. Accessed 3 February 2022.
[41] Ghasemi, M., Anvari, D., Atapour, M., Wormith, J.S., Stockdale, K.C. & Spiteri, R.J. (2021). The application of machine learning to a general risk-need assessment instrument in the prediction of criminal recidivism. Criminal Justice and Behavior, 48, 518-538. CrossRef Google Scholar
[42] Gipson Rankin, S.M. (2021). Technological tethered. Potential impact of untrustworthy artificial intelligence in criminal justice risk assessment instruments. Washington and Lee Law Review, 78, 647-724. Google Scholar
[43] Grabenwarter,C.&Pabel,K.(2021). EuropäischeMenschenrechtskonvention (7th ed.). München: C.H. Beck. CrossRef Google Scholar
[44] 51.Greco, L. (2020). RichterlicheMachtohnerichterlicheVerantwortung. Warum es den Roboter-Richter nichtgebendarf. Rechtswissenschaft, 11, 29-62.CrossRef Google Scholar
[45] Grundies, V. (2016). GleichesRecht für alle? - Eine empirische Analyse lokalerUnterschiede in der Sanktionspraxis in der Bundesrepublik Deutschland. In Neubacher, F. &Bögelein, N. (eds.) Krise - Kriminalität - Kriminologie, Mönchengladbach: Forum Verlag Godesberg, 511525. Google Scholar
[46] Grundies, V. (2018). RegionaleUnterschiede in der gerichtlichenSanktionspraxis in der Bundesrepublik Deutschland. Eine empirische Analyse. In Hermann, D. &Pöge, A. (eds.) Kriminalsoziologie. Nomos: Baden-Baden, 295-316.Google Scholar
[47] Herz, C. (2020). Striving for consistency: Why German sentencing needs reform. German Law Journal, 21, 1625-1648. CrossRef Google Scholar
[48] 55.Hinkkanen, V. &Lappi-Seppälä, T. (2011). Sentencing theory, policy, and research in the nordic countries. Crime and Justice, 40, 349-404. CrossRef Google Scholar
[49] Jehle, J.-M., Lewis, C., Nagtegaal, M., Palmowski, N., Pyrcak-Görowska, M., van der Wolf M. & Zila, J. (2021). Dealing with dangerous offenders in Europe. A comparative study of provisions in England and Wales, Germany, the Netherlands, Poland and Sweden. Criminal Law Forum, 32, 181-245. CrossRef Google Scholar
[50] Kaspar, J., Höffler, K. &Harrendorf, S. (2020). Datenbanken, Online-Votings und künstlicheIntelligenz—PerspektivenevidenzbasierterStrafzumessungimZeitalter von "Legal Tech". Neue Kriminalpolitik, 32, 35-56. Google Scholar
[51] Kehl, D., Guo, P. & Kessler, S. (2017). Algorithms in the criminal justice system. Assessing the use of risk assessments in sentencing. http://nrs.harvard.edu/urn-3:HUL. InstRepos:33746041. Accessed 22 November 2021. Kohn, B. (2021). KünstlicheIntelligenz und Strafzumessung, Baden-Baden: Nomos. Google Scholar
[52] Länderarbeitsgruppe. (2019). Legal Tech: Herausforderungen für die Justiz. Abschlussbericht. https://www.schleswigholstein.de/DE/Landesregierung/II/Minister/Justiz
2646
ministerkonferenz/Downloads/190605_beschluesse/T0PI_11_Abschlussbericht.pdf?__blob=pu blicationFile&v=1. Accessed 8 February 2022.
[53] Langford, M. (2020). Taming the digital leviathan: Automated decision-making and international human rights. American Journal of International Law Unbound, 114, 141-146. Google Scholar
[54] Lappi-Seppälä, T. (2007). Penal policy in Scandinavia. Crime and Justice, 36, 217-295. Google Scholar
[55] Lara, F. &Deckers, J. (2020). Artificial intelligence as a Socratic assistant for moral enhancement. Neuroethics, 13, 275-287. CrossRef Google Scholar
[56] Lee, M. K. (2018). Understanding perception of algorithmic decisions: Fairness, trust, and emotion in response to algorithmic management. Big Data & Society, 5, 1-16. CrossRef Google Scholar
[57] Lindner, B., Neumann, L. &Pohlmann, S. (2020). Comparative Conclusion. In Satzger, H. (ed.) HarmonisierungstrafrechtlicherSanktionen in der Europäischen Union—Harmonisation of Criminal Sanctions in the European Union. Nomos: Baden-Baden, pp. 517-540. Google Scholar
[58] Lyell, D. &Coiera, E. (2017). Automation bias and verification complexity. A systematic review. Journal of the American Medical Informatics Association, 24, 423-431. .CrossRef Google Scholar
[59] Mavronicola, N. (2015). Crime, Punishment and Article 3 ECHR: Puzzles and Prospects of Applying an Absolute Right in a Penal Context. Human Rights Law Review, 15, 721-743. .CrossRefGoogle Scholar
[60] Meyer-Ladewig, J. &Nettesheim, M. (2017). Artikel 8 EMRK. Recht auf Achtung des Privat— und Familienlebens. In: Meyer-Ladewig, J., Nettesheim, M. & v. Raumer, S. (eds.) EMRK: EuropäischeMenschenrechtskonvention—Handkommentar (4 ed.) Nomos: Baden-Baden. Google Scholar
[61] McLuhan, M. & Fiore, Q. (2001). The Medium is the Message. An Inventory of Effects, Corte Madera: Gingki Press Inc. Google Scholar
2647
