From Case Law to Code: Evaluating AI’s Role in the Justice System

🔬 Original article by Alix Faulkner from Encode Canada.

📌 Editor’s Note: This is part of our Recess series, featuring university students from across Canada exploring ethical challenges in AI. Written by members of Encode Canada, a student-led advocacy organization dedicated to including Canadian youth in essential conversations about the future of AI, these pieces aim to spark discussions on AI literacy and ethics.

Introduction

Justice systems have long relied on human discretion to interpret laws, assess evidence, and determine sentencing. Rooted in centuries of legal tradition, judicial decision-making varies across jurisdictions but consistently aims to balance fairness, efficiency, and public trust. As courts face increasing caseloads and demands for consistency, technological solutions have begun reshaping legal processes (Reiling, 2020). Artificial intelligence (AI) now plays a growing role in legal analytics, risk assessments, and even sentencing recommendations (Villasenor & Foggo, 2020). While AI offers potential improvements in efficiency and objectivity, its integration into the judiciary raises pressing questions about bias, accountability, and the fundamental principles of justice (Carnat, 2024).

History of Technology in the Criminal Justice System

The integration of technology into judicial decision-making has evolved over decades, gradually reshaping legal processes. Early efforts to standardize judicial discretion began in the 1970s and 1980s with the implementation of structured sentencing guidelines, which aimed to reduce disparities in criminal sentencing (Tonry, 1996). In the 1990s, the introduction of database-driven legal research platforms, such as Westlaw and LexisNexis, allowed judges and legal professionals to access case law and precedents more efficiently (Susskind, 2008). By the early 2000s, risk assessment algorithms, such as the Correctional Offender Management Profiling for Alternative Sanctions (COMPAS), became widely used to evaluate defendants’ likelihood of recidivism, influencing bail, parole, and sentencing decisions (Berk & Hyatt, 2015). Technology has continued to play a crucial role in shaping judicial practices, providing legal professionals with more efficient ways to analyze case law and assess risk. The integration of such algorithmic assessments marked a shift toward data-driven judicial decision-making, setting the stage for the expansion of AI applications in the justice system (Varghese, 2024).

Practical Applications of AI in Judicial Decision-Making

Case Management

AI-driven case management systems are transforming judicial administration by automating clerical tasks, streamlining workflows, and improving overall court efficiency. These systems assist in scheduling hearings, tracking case progress, and managing legal documents, reducing administrative burden on court staff and expediting proceedings (Cofone, 2021). By utilizing machine learning algorithms, AI can prioritize cases based on factors such as urgency and complexity, ensuring that critical cases receive prompt attention while optimizing resource allocation (Canadian Judicial Council, 2024).

Several jurisdictions have adopted AI-powered case management to address court backlogs. For example, Estonia has introduced an AI-based system to process minor claims disputes, significantly reducing resolution times (Härmand, 2023). Similarly, China has developed “Smart Courts” that integrate AI for case processing, document review, and procedural recommendations, enhancing judicial efficiency (Stern et al., 2021). AI tools also provide predictive analytics that assess potential delays and bottlenecks, allowing courts to proactively address workflow disruptions (Canadian Judicial Council, 2024).

Despite these advancements, concerns remain about the extent to which AI should influence case prioritization and management. Critics argue that over-reliance on automated systems could lead to procedural rigidity and limit judicial discretion (Cofone, 2021). Additionally, ensuring that AI-generated recommendations remain transparent and subject to human oversight is crucial in maintaining judicial accountability (Canadian Judicial Council, 2024). While AI enhances efficiency, it must be implemented with safeguards to uphold fairness and due process.

Legal Research

AI has revolutionized legal research by enabling faster and more accurate retrieval of case law, statutes, and legal precedents. Traditional legal research is often time-consuming, requiring lawyers and judges to manually sift through extensive databases and legal texts. AI-powered tools like ROSS Intelligence and LexisNexis use natural language processing (NLP) to analyze legal documents, extract relevant information, and provide case law recommendations with unprecedented efficiency (Surden, 2025).

One of AI’s most significant contributions to legal research is its ability to identify patterns and predict case outcomes based on historical legal data. For instance, tools like Westlaw Edge employ machine learning algorithms to assess legal arguments and suggest precedents that may strengthen a case (Cofone, 2021). Additionally, AI can help flag inconsistencies in legal reasoning by comparing similar cases and highlighting discrepancies in judicial rulings, improving the consistency of legal interpretations (Sourdin, 2018).

Beyond efficiency, AI-driven legal research democratizes access to legal knowledge. Legal professionals in resource-limited settings can leverage AI tools to access comprehensive legal databases without requiring expensive subscriptions or extensive human resources (Friesen, 2022). However, concerns remain about the reliability of AI-generated legal analyses, as algorithms may miss contextual nuances or reinforce existing biases in case law selection (Ashley, 2017). Despite these limitations, AI’s role in legal research continues to grow, significantly impacting judicial decision-making and case preparation.

Risk Assessment and Recidivism Prediction

AI-driven risk assessment tools are increasingly used in judicial decision-making to evaluate the likelihood of a defendant reoffending. These systems analyze vast datasets, including criminal history, demographic factors, and behavioural patterns, to generate risk scores that inform decisions about bail, sentencing, and parole. One of the most widely known tools is the Correctional Offender Management Profiling for Alternative Sanctions (COMPAS), which has been implemented in U.S. courts to assess defendants’ risk levels (Angwin, 2016). Similarly, Canada’s Justice System is exploring AI-assisted risk assessment models to enhance pretrial detention decisions (Robertson et al., 2020).

Proponents argue that AI-based risk assessments offer greater consistency and efficiency than human decision-making, which can be influenced by cognitive biases and subjective judgments (Kehl et al., 2017). AI models process large volumes of data rapidly, potentially reducing judicial workload and improving resource allocation (Binns, 2018). Additionally, some studies suggest that algorithmic risk assessments, when properly calibrated, can improve the fairness of pretrial release decisions by reducing reliance on cash bail systems (Kleinberg et al., 2017).

However, these tools have faced significant criticism for perpetuating racial and socioeconomic biases embedded in historical crime data (Dressel & Farid, 2018). Research has shown that AI-driven risk assessments may disproportionately categorize Black and Hispanic dependents as high-risk compared to white defendants with similar criminal records (Angwin et al., 2016). The opacity of proprietary algorithms further complicated transparency and accountability, raising concerns about due process violations (Richardson et al., 2014). Despite these challenges, AI-powered risk assessment remains a key component of modern judicial decision-making. Courts and policymakers continue to refine these systems, seeking ways to mitigate bias while leveraging AI’s potential to improve legal outcomes (Barabas et al., 2018).

AI-Powered Sentencing Recommendations

AI-powered sentencing tools are being increasingly explored to assist judges in determining appropriate sentences based on past case data, legal precedents, and offender risk assessments. These systems aim to enhance consistency in sentencing decisions by analyzing vast datasets of judicial rulings and statutory guidelines. For instance, China has implemented an AI sentencing system in certain courts, which suggests penalties based on prior judgments and legal statutes (JI, 2020). In the United States, some jurisdictions have experimented with AI-based sentencing recommendations to standardize outcomes and minimize disparities (Brown et al., 2021).

Advocates argue that AI-driven sentencing tools can help mitigate human biases and promote more uniform sentencing practices (Brennan et al., 2009). Traditional sentencing often varies based on a judge’s personal experience, implicit biases, or courtroom dynamics, whereas AI models provide recommendations grounded in data-driven analysis (Stevenson & Doleac, 2023). Additionally, AI tools can process extensive legal information more efficiently than human judges, potentially reducing sentencing inconsistencies and improving overall judicial efficiency (Moses, 2017).

Despite these advantages, concerns persist regarding algorithmic transparency, fairness, and accountability. Critics caution that AI-driven sentencing tools may reinforce existing disparities, particularly if they rely on biased historical data (Richardson et al., 2014). Studies have shown that predictive sentencing models can disproportionately recommend harsher sentences for marginalized groups, exacerbating systemic inequalities (Eaglin, 2017). Additionally, the lack of clarity in AI-generated decisions raises concerns about judicial discretion and due process rights, as judges may become overly reliant on algorithmic suggestions without fully understanding their rationale (Goodman & Flaxman, 2017).

As AI continues to play a role in sentencing recommendations, legal scholars emphasize the need for transparent algorithms, judicial oversight, and periodic audits to prevent unjust outcomes (Binns, 2018). Policymakers are actively exploring regulatory frameworks to balance the benefits of AI in sentencing while safeguarding defendants’ rights and ensuring fairness in judicial processes (Huq, 2019).

Predictive Analytics for Judicial Outcomes

AI-driven predictive analytics is transforming judicial decision-making by forecasting case outcomes based on historical legal data, precedents, and judge-specific rulings. These systems analyze patterns from thousands of past cases to estimate the likelihood of various legal outcomes, assisting lawyers, judges, and policymakers in making data-informed decisions. For example, the European Court of Human Rights has tested AI models that predict case rulings with an accuracy rate of around 79% by analyzing textual data and judicial reasoning (Aletras, 2016). In the United States, legal tech companies like Lex Machina and ROSS Intelligence use predictive analytics to assess the probability of case success, helping attorneys strategize more effectively (Surden, 2025).

Supporters argue that predictive analytics can enhance judicial efficiency by reducing uncertainty in litigation and enabling courts to allocate resources more effectively (Chen, 2018). AI tools can assist judges in identifying relevant case law, predicting appeal success rates, and suggesting alternative dispute resolution strategies to minimize unnecessary trials (Zeleznikow, 2022). Additionally, these systems can help address judicial backlog issues by prioritizing cases likely to require extensive litigation, ultimately expediting legal proceedings (Ashley, 2017).

However, predictive analytics raises concerns about overreliance on statistical modelling in legal reasoning. Critics warn that AI-generated predictions may reinforce existing biases in judicial decisions, as algorithms are trained on past cases that may reflect systemic inequalities (Cofone, 2021). Furthermore, predictive models struggle with the complexity of legal interpretation, as the law is not solely based on past patterns but also on evolving principles, societal values, and unique case circumstances (Contini et al., 2024). Another major concern is the potential for AI to influence judicial discretion, as judges might feel pressured to align their decisions with algorithmic predictions rather than exercising independent legal reasoning (Kehl et al., 2017).

Ethical and Legal Implications of AI in Judicial Decision Making

The integration of AI into judicial decision-making raises significant ethical and legal concerns, particularly regarding bias, transparency, accountability, and due process. One of the primary ethical challenges is the potential for AI to perpetuate or even amplify existing biases in the justice system. Since AI models are trained on historical legal data, they can inherit systemic disparities present in past judicial decisions, disproportionately affecting marginalized communities (Angwin, 2016). Studies have shown that risk assessment tools like COMPAS have demonstrated racial bias, inaccurately labelling Black defendants as high-risk more frequently than their white counterparts (Dressel & Farid, 2018). Without careful oversight, AI-driven decision-making may reinforce rather than mitigate discrimination in sentencing and bail determinations (Richardson et al., 2014).

Transparency is another critical issue, as many AI models used in judicial processes operate as “black boxes,” meaning their decision-making processes are not easily interpretable by judges, lawyers, or defendants (Binns, 2018). The proprietary nature of many AI tools exacerbates this problem, as developers often withhold algorithmic details under intellectual property protections (Kehl et al., 2017). This lack of transparency challenges the fundamental legal principle that defendants should understand and challenge the evidence against them, raising concerns about due process violations (Goodman & Flaxman, 2017).

Accountability remains a pressing concern, as AI introduces questions about legal responsibility when errors occur. If an AI system produces an incorrect or unjust recommendation, it is unclear whether liability falls on the judge who accepted the recommendation, the developers who designed the algorithm, or the institutions that implemented it (Barabas et al., 2018). This ambiguity complicates efforts to ensure fairness and prevent wrongful convictions or disproportionate sentencing (Huq, 2019). Furthermore, over-reliance on AI in judicial processes may lead to “automation bias,” where judges defer to algorithmic recommendations without critically evaluating their appropriateness in specific cases (Sourdin, 2018).

Finally, AI’s role in judicial decision-making raises broader legal concerns about procedural fairness. The right to an impartial trial and individualized sentencing is a cornerstone of many legal systems, yet algorithmic tools risk reducing complex human circumstances to quantifiable risk scores (Stevenson & Doleac, 2023). Some legal scholars argue that AI-driven sentencing and risk assessments should be subjected to stricter regulatory oversight, including periodic audits and fairness evaluations, to ensure they do not disproportionately harm vulnerable populations (Moses, 2017).

As AI continues to evolve, legal frameworks must adapt to ensure that technological advancements align with ethical principles and fundamental rights. Courts and policymakers must implement safeguards to prevent AI from undermining judicial discretion and the core values of justice (Brennan et al., 2009).

Proposed Solutions and Safeguards

As AI continues to play a role in Judicial decision-making, implementing safeguards is essential to ensure fairness, transparency, and accountability. Several key solutions have been proposed to mitigate risks and enhance the ethical deployment of AI in the legal system.

Algorithmic Transparency and Explainability

AI-driven judicial tools must be interpretable so that legal professionals and defendants can understand how decisions are made. Black-box algorithms, where decision-making processes are opaque, pose significant concerns for due process and fairness (Binns, 2018). The inability to scrutinize AI-generated reasoning may result in erroneous or biased legal outcomes that go unchallenged. To address this, scholars advocate for explainable artificial intelligence (XAI) frameworks, which enhance the interpretability of AI outputs through decision trees, rule-based models, and feature attribution methods (Doshi-Velez & Kim, 2017). Implementing transparency measures, such as mandatory documentation of AI decision-making criteria, can facilitate judicial review and enhance public trust in AI-assisted legal processes (Balakrishnan, 2024).

Bias Mitigating Strategies

AI risk assessments and sentencing tools have been shown to reinforce racial and socioeconomic biases embedded in historical crime data (Angwin, 2016). These biases arise because AI models learn patterns from past cases, reflecting systemic disparities in the criminal justice system. To address this issue, researchers propose fairness-aware machine-learning techniques that actively detect and mitigate discriminatory patterns (Mehrabi et al., 2021). Techniques such as adversarial debiasing and reweighting of training data can help minimize disparities in AI predictions. Additionally, ensuring that training datasets are diverse and representative of various demographics can reduce bias and prevent the over-representation of certain groups in high-risk categories (Richardson et al., 2014). Implementing third-party audits to assess AI fairness before deployment is another crucial step in bias mitigation (Ferrara et al., 2024).

Judicial Oversight and Human-in-the-loop Systems

AI should serve as an advisory tool rather than a determinant of legal outcomes, ensuring that final decisions remain under judicial discretion (Sourdin, 2018). Automated tools like COMPAS, which assess recidivism risk, have been criticized for their opaque methodologies and potential for unfair sentencing recommendations (Kehl et al., 2017). To counteract overreliance on AI, courts should implement review mechanisms where human judges critically evaluate AI-generated risk scores and sentencing recommendations before making a final ruling. This human-in-the-loop approach ensures that AI augments rather than replaces judicial reasoning, preserving legal accountability (Cofone, 2021). Training programs for legal professionals on AI interpretation and ethical concerns can further bolster judicial oversight (National Center for State Courts, 2024).

Regulatory Frameworks and Accountability

Governments and legal institutions must establish standardized guidelines for AI deployment, outlining clear accountability structures in cases of error or bias (Barabas et al., 2018). Some scholars propose independent auditing bodies to assess AI models for fairness, reliability, and compliance with ethical guidelines before they are integrated into court proceedings (Cofone, 2021). Legislative measures, such as the EU’s Artificial Intelligence Act, aim to categorize AI applications based on risk levels and impose strict regulations on high-risk domains like criminal justice (Sachoulidou, 2024). Implementing similar policies worldwide could ensure uniform standards for AI governance in judicial contexts.

Addressing Public Trust and Legal Challenges

Public confidence in AI-assisted judicial decision-making depends on transparency, fairness, and the availability of legal recourse mechanisms. Studies indicate that citizens are more likely to trust AI systems when they are given clear explanations of how decisions are made (Huq, 2019). Ensuring that individuals have the right to challenge AI-driven rulings is a fundamental safeguard against unjust outcomes. Establishing legal pathways for contesting algorithmic decisions, such as requiring AI-generated recommendations to be accompanied by a human justification, can enhance procedural fairness (Rodrigues, 2020). Furthermore, requiring courts to document and disclose their use of AI in legal proceedings can improve public awareness and accountability (Di Porto, 2021).

Conclusion

The future of AI in judicial decision-making stands at a crossroads. While these technologies offer efficiency, consistency, and data-driven insights, they also challenge centuries-old principles of fairness, accountability, and human discretion. Courts may become more streamlined, but at what cost? A system too dependent on algorithms risks reducing justice to a formula—efficient but impersonal, data-driven yet blind to the complexities of human experience.

As AI weaves itself into the fabric of judicial processes, its role must be carefully calibrated. It should serve as an aid, not an arbiter. Transparency, oversight, and ethical design will be the pillars upon which AI’s legitimacy in the legal system rests. Ultimately, the pursuit of justice is not just about precision but about preserving the human element—the capacity for empathy, judgment, and moral reasoning that no algorithm can truly replicate. The challenge ahead is not whether AI will shape the justice system but how we ensure it does so without eroding the very values it was meant to uphold.

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