The AI Moment - And What It Is Costing Us
Artificial intelligence in the legal field is one of the fastest-growing sectors in technology today. GenAI adoption has surged, and with it, cautiously and unevenly, the trust in the accuracy of AI-generated output. But that trust has not always been earned. We have seen too many cases in which lawyers placed blind faith in citations generated by public AI tools, only to discover those citations were entirely fabricated. The most recent example to attract attention involved a major UK law firm, where questions arose over the credibility of documentation submitted, a case that sits in a long and growing line of AI-generated errors finding their way into legal proceedings.
This is not a fringe problem. It is a structural one.~
Development of AI models, platforms, and capabilities has accelerated at a pace never seen before in this industry. New platforms emerge every two months where it once took years to build. For the first time, the legal profession has a genuine opportunity to unlock efficiency, scalability, and most importantly, access to justice at a scale that was previously unimaginable. That is genuinely exciting, and the profession should embrace it.
But speed without structure creates risk. And in a sector where the quality of research directly determines the quality of advice, where a fabricated citation can cost a client their case, the risks of irresponsible AI deployment are not theoretical. They are already playing out in courtrooms.
Who Bears Responsibility and When?
This brings us to the most important question: who is actually responsible when AI gets it wrong in a legal context?
Is it the technology company, which built the system and controls the quality of its output? Is it the law firm, which chose to adopt the tool and deploy it in practice? Is it the individual lawyer, who submitted the AI-generated work without independently verifying it? The answer is not simple and that is precisely why the legal and technology communities need to stop pretending it is.
Responsibility is shared, and it operates at every stage. The technology provider has a duty of care to build systems that are as reliable, transparent, and well-governed as the high-stakes professional context demands. The law firm has an obligation to deploy AI responsibly — with policy, training, and oversight in place before the first query is submitted. And the individual lawyer retains full professional accountability for every piece of work that carries their name. AI does not diminish that accountability. It does not transfer it. It sharpens it.
The MENA region has yet to develop comprehensive AI-specific regulation for legal practice. That gap does not create a responsibility vacuum, it creates a responsibility test. The question is whether the industry will step up before an incident forces the issue, or wait until a high-profile failure demands a regulatory response.
The Accuracy Problem - Why This Is Different?
Previous waves of legal technology changed how lawyers worked, but not the fundamental relationship between the lawyer and the output. A search engine returned results the lawyer then evaluated. A document review tool surfaced materials the lawyer then read and judged. The human remained unambiguously in the loop at every decision point.
Generative AI changes this relationship in a way that previous technology did not. Large language models do not retrieve pre-existing text, they generate new text based on patterns learned across vast bodies of data. The outputs look authoritative. They are written in confident, professional language. They cite specific cases, statutes, and regulations. And sometimes, those citations do not exist.
This is what the research community calls hallucination: the tendency of language models to produce plausible-sounding but factually incorrect outputs. In legal practice, hallucination is not merely embarrassing. It is professionally dangerous. It can mislead a court, harm a client, and expose the practitioner to sanctions.
This is precisely why a responsible legal AI must be built differently from a general-purpose AI. It must be grounded in authoritative legal content, designed to show its reasoning, and built to signal uncertainty rather than paper over it. The duty of care begins at the point of design.
It is therefore on the legal technology company to take that duty of care seriously, to build not the fastest or the most capable AI, but the most trustworthy one.
The Five Pillars of Responsible Legal AI
Trust in legal AI is not a feeling. It is an architecture. It is built - deliberately, verifiably - on five foundations that any responsible legal AI provider must be able to demonstrate.
- Pillar 1: Accuracy and Reliability
Every output from a legal AI system must be grounded in authoritative, verifiable legal sources. In a research context, this means every claim links to a real primary authority, a real case, a real statute, a real regulatory instrument, that a lawyer can independently verify. The output is only as trustworthy as the sources beneath it. - Pillar 2: Transparency and Explainability
Legal professionals need to understand not just what an AI system concluded, but how and on what basis. A tool that returns an answer without a clear audit trail is not a professional tool, it is a black box. Transparency means showing the sources, making the reasoning legible, and being honest when confidence is limited. - Pillar 3: Fairness and Bias Mitigation
AI systems trained on historical data inherit the biases embedded in that data. In legal contexts, this is not an abstract concern. Systems built predominantly on English-language common law content will produce unreliable results when applied to civil law systems, Sharia-influenced frameworks, or Arabic-language instruments. The MENA region's legal complexity demands that this problem be taken seriously, not treated as a footnote. Responsible legal AI requires continuous, active work to identify and address these biases across the full lifecycle of the system. - Pillar 4: Privacy and Data Stewardship
Legal work sits at the intersection of some of the most sensitive information that exists; client instructions, litigation strategy, transaction details, regulatory exposure. Professional confidentiality obligations do not pause for technology. Any AI tool operating in this environment must handle data with governance standards that match the sensitivity of the work. That means explicit policies on data use, clear commitments on whether client interactions are used to train underlying models, and enterprise-grade security as a baseline, not a premium. - Pillar 5: Human Oversight and Professional Accountability
This is the most fundamental principle: AI assists, it does not decide. The professional obligation to the client, to the court, to the rule of law - rests with the lawyer. No AI system, however capable, however well-designed, should be positioned as a substitute for professional judgment. A responsible AI tool is built to reinforce this principle at every step: by prompting verification, by presenting output as a starting point rather than a conclusion, and by keeping the human professional clearly in control.
Conclusion
Responsibility is not a constraint on innovation. It is the price of admission.
We are at an inflection point for AI in legal practice and how this moment is navigated will define the profession's relationship with technology for a generation. The tools are genuinely powerful. The efficiency gains are real. The potential to expand access to legal services across the MENA region, to serve clients and communities that currently go without, is not hypothetical.
But none of that potential is realised if the foundation is not trustworthy. And trust, in this context, is not a marketing statement. It is the result of deliberate choices made at every stage, in how a system is designed, in how it is deployed, and in how the professional who uses it applies their judgment.
The question of responsibility is not going to resolve itself. The MENA region does not yet have the regulatory framework to impose it from the outside. That means the industry, technology providers, law firms, and individual practitioners alike, must hold itself to account. Not because regulators are watching. Because clients are.
A major UK law firm will not be the last firm to face questions about AI-generated documentation. The cases in which AI errors reach courtrooms are not going to stop, they are going to increase, as adoption accelerates and governance lags behind. The difference between the firms that emerge from this moment with their reputation intact and the ones that do not will not be which AI tools they chose. It will be whether they chose to use them responsibly.
The five pillars set out in this article are not a compliance checklist. They are a professional standard. And holding to a professional standard, even in the absence of a legal obligation to do so, is precisely what it means to be a lawyer.
As legal professionals across the region navigate this evolving landscape, building practical AI literacy will be just as important as understanding the technology itself. To support that journey, LexisNexis is inviting legal and business professionals to join the AI Insider Programme, a curated initiative designed to build practical AI fluency across the region.
Join the AI Insider Programme: info.lexis.ae/ai-insider-programme-ln/
Learn more about Lexis+ with Protégé: lexisnexis.com/en-ae/products/lexis-plus-protege
Authored by:
Lara Salem
Head of Content & Data Strategy, LexisNexis MENA
June 2026