As artificial intelligence becomes more embedded in legal practice, it is inevitable that attention turns to adjudication. If machines can draft documents, summarise evidence, and predict outcomes, why should they not assist judges, or even replace aspects of judicial decision-making altogether? The question is often posed as a provocation, sometimes as a threat. It is rarely examined with sufficient care.
At first sight, the appeal is obvious. Courts are under strain. Backlogs grow. Resources are limited. If technology can make the process faster, cheaper, and more consistent, it is tempting to ask why it should not be used. But the attraction of automation conceals a deeper difficulty: adjudication is not merely a technical exercise in applying rules to facts. It is a human practice, embedded in institutions, traditions, and moral expectations.
Judicial decision-making involves more than reaching an outcome. It involves giving reasons, exercising discretion, and doing justice in circumstances that rarely fit neatly into predefined categories. The law is full of open-textured concepts: reasonableness, proportionality, fairness, relevance. These are not bugs in the system. They are features. They allow the law to respond to the complexity of human affairs.
Artificial intelligence systems, by contrast, operate through pattern recognition. They identify correlations in past data and generate outputs that reflect those patterns. This can be extremely useful in some contexts. It can also be deeply misleading in others. Past decisions may reflect bias, error, or historical contingency. Treating them as a reliable guide to what ought to be done risks entrenching precisely the injustices the law exists to correct.
There is also the question of explanation. A judgment is not simply an answer; it is an account. It tells the parties why they have won or lost, and it allows the wider legal system to understand, criticise, and develop the law. This function is not incidental. It is central to legitimacy. A system that produces outcomes without intelligible reasons may be efficient, but it is not judicial.
Proponents of AI-assisted judging sometimes respond that explanation can be generated automatically. A system can be trained to produce reasons that resemble judicial prose. That may be true at a superficial level. But resemblance is not the same as justification. A reasoned judgment reflects the judge’s engagement with the arguments, the evidence, and the consequences of the decision. It is not simply a narrative wrapped around an outcome.
Discretion presents a further problem. Judges are often required to choose between several lawful outcomes. That choice is informed by experience, temperament, and an appreciation of context. It is shaped by an understanding of the parties, the wider legal framework, and the practical effects of the decision. These are not easily reducible to data points. Nor should they be.
There is also a constitutional dimension. Judges are accountable in particular ways. They are appointed through defined processes, bound by ethical obligations, and subject to appeal and scrutiny. Their authority rests not only on correctness, but on independence. Introducing automated systems into adjudication risks obscuring where responsibility lies. If a decision is influenced by a machine, who answers for it?
None of this is to say that technology has no place in the judicial process. On the contrary, courts already rely on a range of digital tools. Case management systems, electronic bundles, transcription software, and research databases all support judicial work. Used properly, AI tools may assist with summarising material, identifying issues, or managing large volumes of information. These are aids to judgment, not substitutes for it.
The danger arises when assistance becomes delegation. A judge who relies uncritically on an automated recommendation risks abdicating responsibility, even if the final decision remains nominally theirs. The more persuasive and authoritative the system appears, the harder it becomes to resist its influence. This is a problem of psychology as much as technology.
There is a temptation to frame the issue as one of inevitability. Courts must modernise, it is said, or be left behind. This is a false dichotomy. The question is not whether courts should use technology, but how, and on what terms. Modernisation does not require the abandonment of judgment. It requires its protection.
For lawyers, this debate matters in two ways. As advocates, they must understand how judges may be using technology, and how that use might shape decision-making. As officers of the court, they have a role in resisting developments that undermine the integrity of adjudication, even if those developments promise efficiency.
Artificial intelligence will continue to change the legal landscape. It may change how cases are prepared, argued, and managed. It should not change what judging is. The core judicial task — to listen, to decide, and to explain — is not a technical problem waiting for an automated solution. It is a human responsibility, and it should remain so.