Legal

From Risk to Reward: How UK Law Firms Can Avoid AI Hallucinations and Build Ethical AI Practices

Generative AI is now part of day to day legal work in the UK, from first drafting research notes to triaging disclosure and summarising contracts. But with opportunity comes risk. Hallucinations (confidently wrong outputs), fabricated citations, privacy leakage, and hidden bias can quickly turn a productivity win into a reputational or regulatory problem.

The firms that win from AI won’t simply be the first to adopt, they’ll be the first to operationalise guardrails: human verification, robust governance, transparent tooling, and measurable controls.

This article lays out a practical, UK centric playbook to reduce AI risk, comply with professional duties, and turn ethics into a client facing differentiator.

Get in touch See AI in action

7 min

Written by Peter Harrison, Director of Proposition Marketing - Legal.

Updated 08/12/2025

What “hallucination” really means in legal work
The UK risk landscape: duties, regulators, and courts
Recent UK case law: lessons from the front line
A Defensible Workflow: Prevent, Detect, Correct
Why these cases matter for your firm
Conclusion: Ethical AI is commercial strategy

1) What “hallucination” really means in legal work

“Hallucination” is AI shorthand for outputs that look plausible but are factually incorrect, fabricated, or unsupported. In legal workflows, that can manifest as:

Invented case citations or misquoted passages.
Mischaracterised statutes or outdated regulations presented as current law.
Overconfident summaries of long documents that omit critical qualifiers.
False risk flags in contract review (e.g., misclassifying innocuous language as high risk).

Why it happens:

Pattern completion vs. truth: Large language models (LLMs) predict the next token. They don’t “know” facts unless grounded in a verified source.
Weak retrieval: If the tool isn’t connected to authoritative materials (or those materials aren’t properly indexed), the model fills gaps from its pretraining.
Prompt ambiguity: Vague instructions or missing context encourages guesswork.
Temperature & safety settings: Creative configurations can increase output variance.

Bottom line: Hallucination is a systemic property of generative models, not a user error. Your defence is process.

2) The UK risk landscape: duties, regulators, and courts

UK practitioners are bound by duties to the court, confidentiality, competence, and integrity. In practice, that means:

You sign it, you own it. Any AI assisted analysis that reaches a client or the court must be independently verified.
Confidential information must not be exposed to consumer grade tools that may train on inputs or lack enterprise safeguards.
Data protection considerations require lawful bases, DPIAs for higher risk processing, and careful cross border data stewardship.
Procurement accountability: When you bring an AI vendor into your stack, you inherit their risks - provenance, logging, redress, and security become your problem.

Courts and regulators are increasingly vocal about fabricated citations and overreliance on AI for legal research. Even where no malice exists, sloppy use can lead to wasted costs orders, client complaints, or SRA referrals. Treat courtroom facing outputs as zero tolerance for hallucinations.

3) Recent UK case law: lessons from the front line

Ayinde v London Borough of Haringey [2025] EWHC 1383 (Admin)

A pupil barrister cited five non existent cases and mis stated the Housing Act 1996 in judicial review grounds. When challenged, the respondent dismissed errors as “cosmetic.” The High Court called this “wholly improper,” issued a wasted costs order, and referred the barrister to the Bar Standards Board. The judgment warned:

“Freely available generative AI tools… are not capable of conducting reliable legal research.”

Hamad Al Haroun v Qatar National Bank [2025] EWHC 1383 (Admin)

In a £90m damages claim, the claimant’s solicitor relied on 45 case citations provided by the client, 18 of which were fictitious and many irrelevant. The solicitor admitted failing to verify the research, which had been generated using publicly available AI tools. The court stopped short of contempt but referred the solicitor to the SRA, stressing that lawyers cannot outsource accuracy to clients, or to AI.

MS v Secretary of State for the Home Department [2025] UKUT 305 (IAC)

A barrister cited a fabricated case (“Y (China)”) during an immigration appeal. When pressed, he doubled down, producing an internet printout with misleading statements. The Upper Tribunal referred him to the Bar Standards Board, noting that unchecked AI outputs can propagate errors through multiple stages of litigation. [lawgazette.co.uk]

Reform UK Candidate Case (Worcestershire Election Challenge)

A litigant in person used AI generated authorities to challenge a local election result. The High Court dismissed the petition and ordered £19,000 costs, noting that cited cases, including one supposedly from 1912 did not exist and were “an invention, indeed a hallucination, of AI.”

Judicial stance: Dame Victoria Sharp, President of the King’s Bench Division, has warned that misuse of AI has “serious implications for the administration of justice” and could lead to sanctions ranging from wasted costs to contempt proceedings.

4) A Defensible Workflow: Prevent, Detect, Correct

Avoiding hallucinations isn’t just about individual safeguards; it’s about creating a repeatable, defensible process that can withstand scrutiny from clients, regulators, and internal stakeholders. A robust workflow should integrate three pillars:

Prevent

Start by reducing the likelihood of hallucinations at the source:

Structured Prompts: Use clear, context-rich prompts and avoid ambiguous language.
Trusted Data Sources: Anchor outputs to verified internal content or curated external datasets.
Model Selection: Choose models optimized for factual accuracy and domain-specific tasks.

Detect

Even the best prevention measures aren’t foolproof. Build in detection steps:

Human-in-the-Loop Review: Require subject matter experts to validate critical outputs.
Automated Fact-Checking: Use AI or third-party tools to cross-reference claims against authoritative sources.
Confidence Scoring: Leverage model-generated confidence indicators to flag low-certainty responses.

Correct

When issues arise, act quickly and transparently:

Error Logging: Document hallucinations and their root causes for continuous improvement.
Rapid Revision: Correct inaccuracies before publication or client delivery.
Feedback Loops: Feed corrections back into prompt libraries and training materials to reduce recurrence.

A defensible workflow isn’t just operational, it’s strategic. It demonstrates due diligence, builds trust, and positions your organization as a responsible AI adopter. Combined with governance, training, and vendor selection, this approach creates a clear differentiation for clients who value reliability.

5) Why these cases matter for your firm

Professional accountability is absolute: Courts will not accept “AI did it” as an excuse.
Regulatory referrals are real: Both barristers and solicitors have been referred to the BSB and SRA.
Costs and reputational damage: Wasted costs orders and public judgments are now routine in AI misuse cases.
Client trust: GCs expect firms to demonstrate robust AI governance, not just adoption.

Conclusion: Ethical AI is commercial strategy

For UK firms, the conversation has moved past “Should we use AI?” to “How do we use it safely, credibly, and at scale?” Hallucinations aren’t a reason to pause, they’re a reason to professionalise. With grounded retrieval, human verification, clear policy, and auditability, you can cut risk dramatically while unlocking better client outcomes, stronger pricing narratives, and a durable reputation for innovation with integrity.