Authored By: Ritish Hans
Faculty of Law, University of Delhi
- Introduction: The Rise of Artificial Intelligence in Legal Decision-Making
Artificial Intelligence has rapidly entered domains that were once considered exclusively human.1 From healthcare diagnostics to financial risk assessment, algorithmic systems increasingly influence decisions that affect individual lives. The legal system has not remained untouched by this technological shift. Courts and judicial institutions across the world are now experimenting with artificial intelligence for case management, analytics, legal research, and, in some jurisdictions, even decision-support systems related to bail and sentencing.
Proponents of artificial intelligence in law often emphasize efficiency, consistency, and objectivity. They argue that algorithmic systems can reduce judicial backlog, eliminate human bias, and enhance accuracy in decision-making. In an era marked by overwhelming case pendency and limited judicial resources, such claims appear attractive. However, the introduction of AI into adjudicatory processes raises a fundamental question: can artificial intelligence truly perform the function of judging, or does adjudication involve qualities that remain inherently human?
Judging is not merely the mechanical application of legal rules to facts. It involves interpretation, discretion, moral reasoning, and accountability. Courts do not merely resolve disputes; they justify outcomes through reasoned decisions that engage with values, rights, and social context. While artificial intelligence can assist judges in performing certain tasks, its growing role in adjudication demands careful scrutiny.
This article argues that artificial intelligence, despite its utility as an assistive tool, cannot replace human judges in adjudication. The limits of AI become apparent when examined through the lenses of judicial reasoning, bias, transparency, accountability, and procedural fairness. Adjudication, at its core, is a normative exercise rooted in human judgment, and reducing it to algorithmic reasoning risks undermining the foundations of justice itself.
- Understanding Adjudication: The Human Foundations of Judicial Reasoning
Adjudication is often misunderstood as a purely technical process governed by statutes and precedents. In reality, judicial decision-making is deeply interpretive and contextual. Judges are required to weigh competing narratives, assess credibility, interpret ambiguous legal provisions, and balance conflicting rights. This process demands discretion, sensitivity, and an understanding of social realities that extend beyond legal texts.
Legal rules rarely apply themselves automatically. Statutes often contain open-textured terms such as “reasonable,” “fair,” or “proportionate,” which require interpretation. Judges must determine what these standards mean in specific factual contexts. This interpretive exercise cannot be reduced to numerical calculation; it involves value judgments shaped by constitutional principles, societal norms, and ethical considerations.
Furthermore, judicial reasoning is justificatory in nature. Courts are expected to provide reasoned judgments explaining why a particular outcome was reached. This requirement is not merely procedural; it is central to the legitimacy of judicial authority. A judgment must persuade not only the parties involved but also the broader legal community that the decision is principled and fair.
Human judges are also accountable for their decisions. Their reasoning is subject to appellate review, public scrutiny, and constitutional standards. This accountability ensures that discretion is exercised responsibly. Adjudication, therefore, is not just about accuracy but about responsibility, explanation, and legitimacy.
- How Artificial Intelligence “Reasons”: An Overview of Algorithmic Decision-Making
Artificial intelligence operates fundamentally differently from human reasoning. Most AI systems used in legal contexts rely on machine learning algorithms that analyze large datasets to identify patterns and correlations. These systems do not “understand” law or justice; they process data based on mathematical models designed to optimize specific outcomes.
Predictive algorithms, for example, assess the likelihood of future events by analyzing past data. In legal systems, such tools have been used to predict recidivism rates, estimate flight risks, or recommend sentencing ranges. While these predictions may appear objective, they are entirely dependent on the quality and nature of the data on which they are trained.
Unlike human judges, AI systems do not engage in moral reasoning.2 They cannot interpret principles, empathize with circumstances, or reassess values considering new social realities. Their outputs are the product of statistical inference, not normative judgment. This distinction is crucial when evaluating their suitability for adjudication.
Moreover, algorithmic systems lack the ability to justify decisions in the manner required by courts. While an AI system may produce an outcome, it cannot meaningfully explain why that outcome is just or appropriate in moral or legal terms. This limitation becomes particularly significant in contexts where liberty, dignity, and fundamental rights are at stake.
- The Problem of Bias and Data Dependence in AI-Driven Adjudication
One of the most frequently raised concerns regarding AI in adjudication is algorithmic bias. Contrary to popular belief, artificial intelligence is not inherently neutral. Algorithms reflect the data on which they are trained, and historical data often contains embedded social and institutional biases.
In criminal justice systems, datasets may reflect patterns of over-policing, discriminatory enforcement, or socio-economic inequalities. When such data is used to train predictive models, the resulting systems risk perpetuating and amplifying existing injustices. An algorithm trained on biased data does not correct inequality; it normalizes it.
Bias in AI systems is particularly troubling in adjudicatory contexts because it operates invisibly. Unlike human judges, whose biases can be challenged through reasoning and appeal, algorithmic biases are often hidden within complex models. This opacity makes it difficult for affected individuals to contest decisions or even identify the source of unfairness.
The reliance on historical data also limits the capacity of AI to adapt to changing social values. Legal systems evolve through judicial interpretation, which responds to new understandings of rights and justice. AI systems, however, are backward-looking by design. They predict the future based on the past, making them ill-suited to drive progressive legal development.
- Transparency, Accountability, and the ‘Black Box’ Challenge
Transparency is a cornerstone of the rule of law. Judicial decisions must be open to scrutiny, and their reasoning must be accessible. Many AI systems, particularly those using deep learning techniques, operate as “black boxes,” producing outputs without intelligible explanations.3
This lack of explainability poses serious challenges for adjudication. A person affected by a judicial decision has the right to understand why that decision was made. If an algorithm influences or determines the outcome, but its reasoning cannot be explained, the right to a reasoned decision is undermined.
Accountability further complicates the use of AI in adjudication. If an algorithmic system produces an unjust outcome, determining responsibility becomes difficult. Is the judge accountable for relying on the system? Is the developer responsible for its design? Or does responsibility diffuse across institutions, leaving no clear locus of accountability?
Such ambiguity is incompatible with legal systems that demand clear attribution of responsibility. Justice requires not only correct outcomes but also identifiable decision-makers who can be held accountable.
- Due Process and Fair Trial Concerns in AI-Assisted Adjudication
International human rights law emphasizes procedural fairness as an essential component of justice.4 The right to a fair trial includes the right to be heard, the right to reasoned decisions, and the right to challenge adverse outcomes.5 The integration of AI into adjudication raises questions about whether these guarantees can be preserved.
If algorithmic systems influence judicial decisions, parties may be unable to meaningfully challenge the basis of those decisions. Without access to the logic underlying an algorithmic output, the right to contest evidence and reasoning becomes hollow. This undermines procedural equality and the adversarial process.
Furthermore, the due process is not solely concerned with efficiency. While AI may accelerate decision-making, speed cannot come at the cost of fairness. Justice delayed may be justice denied, but justice automated without accountability risks becoming justice distorted.
Courts must therefore ensure that technological tools do not erode procedural safeguards. The use of AI must be carefully regulated to preserve the integrity of adjudication and protect fundamental rights.
- International Approaches to AI in Adjudication
Globally, legal systems have approached AI in adjudication with caution. While many jurisdictions encourage the use of technology for administrative efficiency, there is widespread reluctance to allow fully automated judicial decision-making.
The European Union, for instance, has adopted a risk-based approach to regulating artificial intelligence.6 Systems used in the administration of justice are classified as high-risk, requiring strict oversight, transparency, and human control. This reflects an acknowledgment that adjudication involves values that cannot be fully delegated to machines.
International organizations and human rights bodies have similarly emphasized the need for human oversight in AI-assisted decision-making.7 The prevailing international consensus recognizes that while AI can support judicial functions, it should not replace human judgment.
- Artificial Intelligence as an Assistive Tool: Defining the Appropriate Role of Technology
Despite these limitations, rejecting artificial intelligence entirely would be neither realistic nor desirable. AI has significant potential to improve access to justice when used appropriately. It can assist judges by streamlining case management, facilitating legal research, and identifying relevant precedents.
When deployed as an assistive tool rather than a decision-maker, AI can enhance judicial efficiency without undermining core values. The key lies in maintaining human oversight and ensuring that final decisions rest with accountable judges.
Defining the boundaries of AI’s role in adjudication is therefore essential. Technology should support, not substitute, judicial reasoning. Courts must retain control over decision-making processes and ensure that technological tools align with constitutional and human rights principles.
6.European Commission, Proposal for a Regulation of the European Parliament and of the Council Laying Down Harmonized Rules on Artificial Intelligence (Artificial Intelligence Act) COM (2021) 206 final. 7. Council of Europe, European Ethical Charter on the Use of Artificial Intelligence in Judicial Systems and Their Environment (2018).
- Conclusion: Why Judicial Reasoning Must Remain Fundamentally Human
Artificial intelligence represents a powerful tool with the potential to transform legal systems. However, adjudication is not a task that can be reduced to algorithmic efficiency. Judicial decision-making involves discretion, moral reasoning, accountability, and the articulation of reasons — qualities that remain inherently human.
While AI can assist courts in managing caseloads and improving administrative efficiency, entrusting machines with the authority to judge risks eroding the foundations of justice. Law is not merely about predicting outcomes; it is about justifying them in a manner consistent with human dignity and constitutional values.
The future of adjudication lies not in replacing judges with machines but in carefully integrating technology in ways that enhance, rather than diminish, human judgment. Justice must remain a human enterprise, guided by reason, empathy, and responsibility.
Reference(S):
OECD, Artificial Intelligence in Society (OECD Publishing 2019).
Mireille Hildebrandt, ‘Law as Computation in the Era of Artificial Legal Intelligence’ (2018) 68 University of Toronto Law Journal 12.v
Frank Pasquale, The Black Box Society: The Secret Algorithms That Control Money and Information (Harvard University Press 2015).
United Nations Human Rights Council, The Right to Privacy in the Digital Age, UN Doc A/HRC/39/29 (3 August 2018).
International Covenant on Civil and Political Rights (adopted 16 December 1966, entered into force 23 March 1976) 999 UNTS 171 art 14
