Should AI Be Allowed in Judicial Decision-Making?

17.03.2026

AI Enters the Courtroom

In courtrooms across the United States, a software system called COMPAS has been quietly influencing the fates of defendants for years — producing risk scores that judges cite when deciding sentences, bail, and parole. In China, the Supreme People's Court has mandated that every court in the country deploy AI tools for judicial support by the end of 2025, with full integration of AI into judicial processes projected by 2030. In Estonia, an AI system has been issuing binding verdicts in small claims disputes, with the option for human appeal. In Brazil, Spain, and Singapore, courts are deploying AI tools to assist with legal research, evidence review, document transcription, and procedural guidance.

Artificial intelligence has arrived in the judiciary — not as a distant future possibility, but as a present operational reality. And with it has arrived a set of questions that go to the heart of what it means for justice to be administered.

The judiciary is not merely another sector of public administration. Judicial decisions directly determine liberty and imprisonment, property and poverty, rights and deprivations. They are the institutional mechanism through which the rule of law is made tangible in individual lives. When an algorithm influences a sentence, the consequences for the person before the court — and for the legitimacy of the legal system itself — are profound.

This article examines the growing use of AI in judicial systems worldwide, evaluates both the promise and the peril of these applications, and asks the question that the technology itself cannot answer: should artificial intelligence be allowed to play a role in judicial decision-making, and if so, under what conditions?

Current Applications of AI in Judicial Systems

The United States: Risk Assessment in Criminal Justice

The United States provides the most extensively documented and debated example of AI in judicial contexts. Risk assessment instruments — algorithmic tools that estimate the probability that a defendant will reoffend — are now used in some form across 46 states, informing pretrial detention decisions, sentencing, and parole. The most prominent of these, COMPAS (Correctional Offender Management Profiling for Alternative Sanctions), uses questionnaire responses and demographic data to generate risk scores between 1 and 10, classifying defendants as low, medium, or high risk.

The tool's use in sentencing came to national prominence through the landmark case State v. Loomis, in which the Wisconsin Supreme Court ruled in 2016 that COMPAS scores could be considered by judges during sentencing — but must be accompanied by warnings about the tool's limitations and must not be used as the sole or determinative factor. This qualification proved significant: in practice, courts have struggled to contain the influence of algorithmic scores once they appear in presentence reports. As one Wisconsin judge acknowledged at an appeals hearing, his sentence was affected by the COMPAS assessment, suggesting he would have imposed a lighter sentence without it.

The case also exposed a structural accountability problem: COMPAS is proprietary software, and its underlying algorithm is a trade secret. Defendants subject to its assessments cannot examine how it calculated their risk score, challenge the methodology, or verify that inputs were accurate. This opacity, critics argue, violates basic due process principles: a defendant's sentence is influenced by evidence they cannot scrutinize, produced by a process they cannot understand.

China: The Smart Court System

China's engagement with AI in its judicial system is more comprehensive in scale and more explicit in state direction than any other jurisdiction. The "Smart Courts" strategy — developed under the Supreme People's Court — represents a far-reaching digitization and automation of Chinese courts that goes beyond administrative efficiency tools to encompass decision-making support at the core of the judicial process.

China launched a national judicial AI platform in late 2024 that has gathered 320 million pieces of legal information including court rulings, cases, and legal opinions. AI systems now assist judges with case management, electronic file circulation, hearing voice recognition, judgment drafting, and legal research. At the Hainan High People's Court, adopting an AI judgment-drafting assistant has enabled judgments to be produced in 50% less time overall, with written judgments taking 70% less time. At the Kunshan People's Court, a judge described her AI assistant as "a reliable companion on the road toward judicial fairness," noting that it generates more than 70% of a judgment draft with a single click — though human refinement remains required.

The Chinese government has been explicit that AI systems will not replace judges in determining the outcome of cases. Official guidance states that rulings must always be made by judges and that AI outputs serve as supplemental references rather than binding conclusions. Yet the system's deeper integration — including direct connectivity between court AI platforms and police databases, AI tools that filter cases based on political sensitivity, and targets for AI to provide "high-level support" for all judicial processes by 2030 — raises questions about the actual boundaries of human judgment in a system designed to embed algorithmic guidance at every stage.

Estonia: The AI Judge Experiment

Estonia has attracted significant international attention for its experiment with algorithmic adjudication in small claims disputes. An AI system, trained on thousands of past rulings, has been deployed to adjudicate cases valued below a defined monetary threshold — the judge at the Hainan court noted claims under approximately €7,000. Parties submit evidence digitally, the AI analyzes relevant precedents and contractual terms, and issues a binding verdict that remains appealable to a human judge.

Proponents note that the system has dramatically reduced case resolution times and generated meaningful savings in court administration costs. The binding nature of the AI verdicts, subject to human appeal, represents one of the most direct examples of algorithmic judicial authority anywhere in the world. Yet even its advocates acknowledge limitations: the system struggles with cases involving contextual nuance, emotional complexity, or fact patterns that diverge from its training data. Cases involving custody disputes, for example, require forms of judgment that fall outside the system's competence.

Legal Analytics Platforms

Beyond formal judicial systems, AI tools have transformed how legal research and case analysis are conducted across many jurisdictions. Platforms that use machine learning to identify relevant precedents, predict litigation outcomes, analyze contract language, and flag legal risks have become standard tools in major law firms and are increasingly available to courts. In 2024, Singapore's judiciary deployed a customized AI system for its Small Claims Tribunals, providing parties with legal advice, procedural guidance, document translation, and claim valuation services — functioning as an access-to-justice tool rather than a decision-maker. Brazil's Superior Council of Labour Justice launched Chat-JT in February 2025, a generative AI tool that assists judges, court staff, and interns by automating legal research, document analysis, and drafting of standardized summaries.

The Growth of AI in Legal Technology

The integration of AI into judicial systems is part of a broader transformation of the legal sector. AI-powered legal research tools, automated document review systems, and predictive analytics platforms have fundamentally changed the economics and pace of legal work. Research that previously required weeks of associate attorney time can be accomplished in hours. Contract review that once demanded experienced paralegal attention can be substantially automated.

This growth carries both opportunity and risk for judicial systems specifically. As AI tools become more capable, the pressure to integrate them into court operations — for efficiency gains and backlog reduction — will intensify. Court systems in many countries face genuine crises of capacity: in the United States, federal judiciary filings have surged significantly since 2020, with judges managing very large caseloads. Against this backdrop, AI tools that can accelerate legal research, automate document processing, and assist in drafting routine judgments represent real operational value.

The EU AI Act classifies AI systems used in the administration of justice — including tools that prepare court rulings — as high-risk applications, requiring conformity assessments, technical documentation, transparency obligations, and human oversight mechanisms before deployment. This regulatory framework, whose requirements will become fully applicable by August 2026, represents the first binding legal regime specifically addressing AI use in judicial contexts in any major democratic jurisdiction. The Act also explicitly prohibits AI systems that make risk assessments of individuals to predict the likelihood of criminal offending based solely on personality profiling — a provision directly relevant to the COMPAS debate.

Arguments Supporting AI in Judicial Decision-Making

Efficiency and Backlog Reduction

The most immediate practical argument for AI in judicial systems is capacity. Court backlogs impose enormous costs on individuals and societies: delayed justice, prolonged pretrial detention, resources consumed by protracted proceedings. AI tools that automate routine tasks — transcription, document categorization, case filing, precedent research, draft judgment generation — free judicial time for the work that requires human judgment. Estonia's AI system resolved small claims disputes in 48 hours that would previously have taken 90 days. China's judgment-drafting AI reduced writing time for certain documents by 90%. These are not marginal gains.

Reducing Inconsistency and Human Bias

A persistent critique of human judicial decision-making is that it is inconsistent in ways that are both unfair and arbitrary. Studies have found that parole approval rates drop to near zero immediately before judges eat lunch, recovering after the meal — a pattern suggesting that decisions of enormous consequence are influenced by the judge's physiological state rather than the merits of the case. Sentencing decisions for similar offenses can vary enormously between judges in the same jurisdiction, and racial disparities in sentencing have been extensively documented.

Proponents of algorithmic assistance argue that systematic application of legal rules, calibrated against large datasets of prior decisions, could reduce these inconsistencies. This argument has some empirical support: a 2024 study at Tulane University found that AI-assisted sentencing cut jail time for low-risk offenders — though racial bias persisted. The potential to partially reduce arbitrary inconsistency is genuine, even if algorithmic systems introduce their own forms of systematic bias.

Enhanced Analytical Capacity

AI systems can analyze vast datasets of case law and identify patterns that human analysis would miss over realistic timeframes. For legal research, this is potentially transformative: a judge facing a novel legal question can receive a comprehensive survey of relevant precedents across multiple jurisdictions in seconds rather than days. For evidence analysis in complex commercial or technical cases, AI tools can process and cross-reference documentary records at scales that would otherwise require armies of paralegals. The OECD has found that AI tools can contribute to judicial efficiency and improve the consistency and accountability of government activities when properly designed and overseen.

Criticism and Ethical Concerns

Algorithmic Bias: The COMPAS Problem

The most extensively documented problem with AI in judicial contexts is the reproduction and amplification of historical bias. The COMPAS controversy crystallized the issue. ProPublica's landmark 2016 investigation found that COMPAS labeled Black defendants as high risk for future crime at roughly twice the rate of their white counterparts who did not reoffend — while incorrectly labeling white defendants as low risk at a higher rate than Black defendants who did reoffend. The tool was simultaneously calibrated to be accurate in its overall predictions and systematically discriminatory in its error distribution.

This controversy illuminated a fundamental mathematical problem: it is impossible to simultaneously satisfy two intuitive definitions of algorithmic fairness. Predictive parity — where a given score means the same thing for all demographic groups — and equal error rates across groups cannot both be achieved when the underlying outcome rates differ between those groups. As researchers have demonstrated, this is not a flaw that can be corrected through better engineering; it reflects an inherent normative choice about whose errors are acceptable. That choice should be a democratic and legal decision, not one buried in proprietary code.

Subsequent research has confirmed and extended these concerns. A 2024 study found that the use of COMPAS in Broward County reduced overall confinement rates across demographic groups, but exacerbated differences in confinement between racial groups, deepening racial disparity. A 2025 paper in Artificial Intelligence and Law demonstrated that COMPAS predictions favor jailing over release and show anti-Black and anti-young bias, while also showing that these biases can be substantially reduced — an option that COMPAS's developers had not provided.

The Black Box Problem and Due Process

Many AI systems, particularly those based on deep learning, operate through processes that are opaque even to their developers. When a judge's decision is influenced by an AI system whose reasoning cannot be explained, a fundamental requirement of judicial legitimacy is compromised: the ability to know, understand, and challenge the basis of decisions that affect fundamental rights.

In State v. Loomis, the defendant argued that his sentence was unconstitutionally determined in part by a tool whose methodology was a trade secret, preventing him from challenging its accuracy or scientific validity. The Wisconsin Supreme Court did not fully accept this argument, but acknowledged the legitimacy of the concern and required that courts accompany COMPAS scores with warnings about the tool's limitations. More recently, in 2024, a New Jersey federal judge withdrew an opinion in a shareholder lawsuit after discovering it included AI-generated citations to non-existent cases — inserted by a staff member using an AI drafting tool. Such incidents illustrate how automation bias — the tendency to trust machine-generated outputs without adequate scrutiny — can compromise judicial integrity even in the absence of deliberate malfeasance.

The EU AI Act's classification of judicial AI tools as high-risk applications requiring detailed technical documentation and transparency obligations reflects a regulatory judgment that black-box systems in judicial contexts are unacceptable — a position that enjoys broad support in legal scholarship. The Act also specifically prohibits AI that makes criminal offense predictions based solely on personality profiling, drawing a direct line between the COMPAS controversy and European regulatory policy.

Accountability Vacuums

Traditional judicial accountability rests on the principle that a named human judge, bound by law and professional ethics, makes a decision that can be explained, appealed, and reviewed. When algorithmic systems influence judicial outcomes, this accountability chain fractures. If a defendant receives an unjust sentence influenced by a biased algorithm, the developer of the algorithm is shielded by proprietary trade secret claims, the judge claims to have exercised independent judgment, and the defendant has no clear target for legal challenge.

Legal scholarship has identified this as an "accountability gap": a space where harmful decisions are made but no actor bears clear responsibility. Recent academic analysis of COMPAS has traced this gap to three persistent factors — model opaqueness, lack of interpretability, and the tendency toward automation bias — and has argued that these problems are not resolved by technological advances alone, including the shift to generative large language models.

The Impossibility of Algorithmic Empathy

Beyond bias and opacity, there is a deeper philosophical objection to AI in judicial decision-making: the claim that justice requires forms of understanding that algorithms cannot provide. Judicial decisions — particularly in criminal matters — require not merely the application of rules to facts, but the exercise of moral judgment: proportionality, mercy, the assessment of genuine remorse, the weighing of competing social interests that no legal text fully resolves.

As one analysis in the legal governance literature put it: a machine cannot perceive remorse in a defendant's voice, nor can it appreciate the nuances of rehabilitation, deterrence, or mercy. The judicial oath requires more than logical consistency; it demands moral discernment. Courts do not merely decide facts; they exercise a form of practical wisdom that is inseparable from the judge's humanity — their capacity to understand the human situation before them.

Ethical and Legal Considerations

The ethical questions surrounding AI in judicial decision-making cluster around several principles that are fundamental to the rule of law.

Equality before the law requires that similarly situated individuals receive similar treatment. AI systems offer the possibility of reducing arbitrary human inconsistency — but at the risk of substituting systematic algorithmic discrimination. Whether the replacement of one form of inequality with another constitutes progress depends on which inequalities are prioritized in system design: a normative choice that must be made transparently and democratically.

Transparency in judicial reasoning is a foundational requirement of fair legal process. Courts must not only decide correctly but explain their reasoning in terms that the parties can understand and challenge. An AI system that generates outputs without interpretable reasoning violates this requirement. The EU AI Act's transparency obligations for high-risk AI systems, and the CJEU's ruling in Dun & Bradstreet Austria (2025) that data controllers must provide concise, intelligible explanations of automated processing logic, both reflect this principle in emerging legal doctrine.

Human oversight of consequential decisions is increasingly recognized in international legal frameworks as a non-negotiable requirement in high-stakes contexts. The EU AI Act prohibits AI systems that make criminal risk predictions based solely on algorithmic profiling. China's official guidance explicitly states that rulings must always be made by judges. The Canadian Judicial Council's 2024 guidelines allow AI in judicial administration but prohibit its use in legal reasoning and decision-making. A convergence toward the principle that human judgment must remain final in judicial contexts is visible across multiple legal systems, even as implementation diverges.

Protection of fundamental rights requires that individuals facing judicial processes retain meaningful ability to understand, challenge, and appeal decisions. Where AI systems influence judicial outcomes through opaque processes protected by trade secret claims, this right is substantively compromised. The right to a fair trial — guaranteed by the Universal Declaration of Human Rights and in various forms by national constitutions and regional human rights instruments — encompasses a right to understand the evidence and reasoning on which adverse decisions are based.

The Future of AI in Courts

The trajectory of AI in judicial systems points toward deeper, more widespread integration, not retreat. The economic, efficiency, and analytical arguments for AI judicial assistance are compelling, and the institutional pressures driving adoption — court backlogs, resource constraints, growing legal complexity — are not diminishing. The question is not whether AI will be present in future courts, but in what roles, under what constraints, and with what safeguards.

Several possible futures warrant consideration.

AI-assisted legal research — in which AI tools provide judges and lawyers with comprehensive, up-to-date surveys of relevant precedents and doctrinal analysis — is the least controversial application and the one already most widespread. The risks here are primarily about accuracy and hallucination (AI systems generating plausible-sounding but non-existent citations) rather than bias or accountability, and are addressable through appropriate verification requirements and professional accountability standards.

AI-supported administrative processing — document classification, scheduling, transcription, case management — imposes minimal risks to fundamental rights and offers clear efficiency benefits. This category should be relatively uncontroversial, provided that accuracy standards are met.

AI-assisted judgment drafting — where AI generates draft judgments that human judges review, modify, and approve — represents a higher-stakes application. The risk of automation bias, where judges over-rely on algorithmically generated drafts without exercising independent judgment, is documented and real. Appropriate human oversight requirements, documentation obligations, and verification standards can mitigate this risk, but cannot eliminate it.

AI-based predictive analytics and risk assessment — the COMPAS paradigm — represents the application with the most documented risks and the most contentious ethical record. These tools will likely continue in use, but they require fundamental reforms: public access to algorithmic methodology, independent auditing for bias, explicit prohibition on use as sole or primary determinative factors, and clear rights for defendants to challenge inputs and methodology.

Autonomous AI adjudication — systems that issue binding decisions without human review — is currently limited to low-value disputes in a small number of jurisdictions. The conditions under which such systems can operate legitimately are narrow: minimal value cases, clear factual determinations that require no contextual judgment, and robust appeal mechanisms to human courts. Extension beyond these limits would require regulatory frameworks and democratic deliberation that do not currently exist.

AI as a Tool, Not a Judge: A Final Reflection

The question posed in this article's title — should AI be allowed in judicial decision-making? — admits no simple answer, because "judicial decision-making" is not a single thing. Administrative scheduling is different from legal research; legal research is different from risk assessment; risk assessment is different from sentencing. AI may be appropriate for some of these functions and deeply problematic for others, and the appropriate response to each requires careful, context-specific analysis rather than wholesale embrace or rejection.

What the evidence does establish is that AI should not be a judge — not in the sense of bearing final authority over decisions that determine human liberty, rights, and welfare. Judicial decisions require forms of understanding, empathy, moral reasoning, and accountability that algorithmic systems cannot reliably provide. A machine that cannot perceive remorse, cannot appreciate context, cannot exercise mercy, and cannot be held personally responsible for its errors is not capable of administering justice in any meaningful sense of that word.

What the evidence also establishes is that AI can genuinely assist human judges — reducing inconsistency, accelerating research, processing information at scales impossible for human cognition, and identifying patterns that enhance the quality of judicial reasoning. Used as an instrument of judicial support, rather than a substitute for judicial judgment, AI has real value in legal systems.

The conditions for legitimate AI use in judicial contexts are becoming clearer through regulatory development and legal scholarship: transparency about when and how AI systems are used, meaningful ability for affected parties to challenge AI-generated inputs, independent auditing of algorithmic systems for bias, prohibition on using AI as the sole or determinative factor in high-stakes decisions, and preservation of human judicial authority as the final point of accountability in all consequential determinations.

These conditions are not yet met in most jurisdictions. The COMPAS controversy shows what happens when they are not. Building judicial AI governance that meets them is one of the most urgent tasks in contemporary legal policy — because the technology is already in the courtroom, whether governance frameworks are ready for it or not.