The legal profession is undergoing a moment of reckoning with artificial intelligence (AI). As AI systems rapidly enter legal workflows — from document review to sentencing algorithms — the question has moved from whether AI belongs in the courtroom to how far it should go. Could AI Judges be the next radical step? Could they help create a justice system that is not only gender-neutral, but fundamentally bias-neutral?
It’s a provocative idea and one worth exploring.
A Shifting Landscape
The debate over AI in the legal field is intensifying. In June, the High Court issued a stark warning to lawyers over the “misuse” of AI-generated content in pleadings, citing a case in which 18 out of 45 case citations submitted in a £89 million dispute turned out to be fabricated by AI. The judiciary has responded with updated guidance, urging legal professionals to treat AI as a tool — not a source of truth — and to remain ultimately responsible for the outputs they use.
In parallel, judicial figures like the Master of the Rolls have spoken positively about AI’s potential to increase access to justice, provided its use is transparent, accountable, and safe. The UK judiciary has also published formal guidance on AI, making clear that judges may use generative tools for drafting or research — but not (yet) for decision-making.
And yet, there’s increasing pressure to think beyond that. A recent Guardian investigation found evidence of victim-blaming language used by Family Court Judges in domestic abuse cases. In some judgments, stereotypical views about women’s credibility or behaviour were allowed to influence outcomes. It’s the kind of unconscious bias our gender-neutral Court proposal aimed to dismantle.
So here’s the question: if we want rulings that are free of gender, class, and racial prejudice — might AI offer a path?
A Case For AI Judges
1. Bias Elimination at Scale
AI systems, if designed properly, can apply legal rules consistently and without prejudice. Where human judges bring life experience, they also bring implicit bias. Whether it’s gendered assumptions about parenting or racial disparities in sentencing, these influences often operate below the surface — despite training and awareness.
AI doesn’t “see” gender or ethnicity unless told to. It doesn’t stereotype. And it could be programmed to anonymise data inputs, removing names, locations, or backgrounds that might trigger unconscious associations. In this sense, AI could build on the gender-neutral principles we previously explored — offering not just neutral language, but neutral reasoning.
2. Consistency and Transparency
AI can analyse thousands of prior cases to spot and replicate consistent patterns of decision-making. This could help reduce discrepancies between Courts or regions, and provide a reliable benchmark for parties and practitioners.
AI could also audit itself: identifying outliers in sentencing, generating explanations for each decision, and allowing transparency in a way that opaque human judgments sometimes resist.
3. Capacity and Access to Justice
Let’s not forget the practical benefits. Court backlogs are growing. Legal aid is shrinking. For arguably “low-level” disputes — small claims, immigration appeals, even housing tribunals — AI Judges could help reduce delays and free up human judges for the most complex or sensitive work.
Pilot schemes abroad have shown promise. In Estonia, a government-backed system has tested automated decisions in minor contract disputes. In China, AI-powered “cybercourts” have processed thousands of online cases. While far from perfect, these experiments suggest that AI can play a meaningful judicial role — under the right conditions.
A Case Against AI Judges
But the path to AI justice is littered with hazards.
1. Garbage In, Garbage Out
AI is only as unbiased as the data – or people – it learns from. If the system is trained on historical case law rife with discrimination — against women, people of colour, or working-class defendants — it may not remove bias but reproduce it, just in digital form.
We’ve already seen this in the US, where AI risk assessment tools like COMPAS may have been shown to unfairly label Black defendants as higher-risk, leading to longer sentences. Without careful oversight, AI can quietly encode the very inequities we hope to fix.
2. No Human Judgment
Justice is not just a matter of logic — it’s also about empathy, discretion, and the ability to weigh the human context. In Family Law especially, decisions often hinge on subtle dynamics: a child’s emotional needs, a survivor’s credibility, a pattern of coercive control.
AI is currently ill-equipped to handle these nuances. It can parse language and identify legal elements, but it cannot understand or even recognise trauma or build rapport. Critics argue that replacing Judges with machines risks losing the very humanity that justice demands.
3. Accountability and Trust
When a Judge errs, there is a name, a judgment, and an appeal process. But when an AI system makes a wrong call — who is accountable? The software engineer? The judicial body that approved its use? The party who relied on it?
Trust in the justice system depends on openness. “Black-box” algorithms — especially those developed by private companies — challenge that trust. Without explainability and oversight, litigants may be left wondering whether their fate was determined by logic or lottery.
Additionally, we know that a recent MIT Report concluded that 95% of AI is failing, and the discourse surrounding that tells us that one of the biggest issues is the failure of individuals to interact with – and thus, learn from – the material generated by AI. This in itself breeds a lack of accountability, and ownership, of not just material prepared but decisions made.
Where Do We Go From Here?
Despite these challenges, the promise of AI justice should not be dismissed. Rather than full automation, we might envision a model of augmented justice — where AI acts as a co-pilot, not a captain.
For example, AI could:
- Draft judgments for human approval;
- Flag inconsistencies in judicial reasoning;
- Identify language that may suggest bias;
- Suggest precedent or summarise submissions.
Meanwhile, low-stakes cases — such as certain administrative or procedural matters — could serve as a testbed for greater AI involvement.
Critically, any system must be auditable, transparent, and accountable. Bias detection must be built in. Litigants must retain rights of appeal. And Judges — or at least judicial panels — must retain final responsibility for outcomes. If a Judge wishes to use an AI tool, at the heart of Justice, we suggest that they must own the final output which ultimately bears their name.
A New Kind of Neutrality
AI may offer a broader version of that principle: a form of structural neutrality that goes beyond gender to address race, class, mental health, and other axes of bias.
Of course, neutrality is not the same as fairness. But it’s a step.
The legal profession faces a choice. Do we double down on improving human decision-makers — through training, guidance, and cultural change? Or do we experiment with machines that, while imperfect, may be free from many of our worst prejudices?
Perhaps it’s not either/or. Perhaps the path forward is human–AI collaboration — combining the strengths of both.
As we navigate this rapidly changing landscape, one thing is clear: justice must not only be done, it must be seen to be fair. If AI can help us get there, should we at least be willing to try?
James Legg, Counsel at IMD Solicitors LLP, and Antonia Kirby, Solicitor at Moore & Tibbits
