GENERATIVE AI CHATBOTS burst into public consciousness with promises of revolutionizing services, including legal services. Yet in practice, their outputs (providing legal services) often mislead more than enlighten. Consider a case in New York: an attorney cited entirely fictional cases in a court brief - all generated by ChatGPT - and faced sanctions.
It has now become a very common understanding that AI “hallucinates;” meaning it invents fictitious precedents or law(s) that “sound good,” but are false. These incidents are not mere anecdotes. They illustrate a fundamental truth about current AI chatbots: they manipulate the statistical patterns of language rather than apply legal reasoning grounded in legislations and cases. As law schools teach, legal argument(s) requires rigorous issue-spotting, doctrinal analysis, and strict adherence to jurisdictional rules. These are qualities which AI chatbots do not inherently possess.
It is quite relevant to unpack why AI chatbots struggle in the legal domain. There exist technological limitations in large language models (‘LLMs’). The crucial difference between narrow AI tools and the “general AI” is notable. These limitations collide with the law’s demand for precise, consistent, jurisdiction‐specific direction. A report by the New York State Bar Association confirms that generic chatbots answer legal questions incorrectly, often by filling gaps with invented content. Unlike factual domains, the law does not tolerate such hallucinations. There lies a need to explore the ethical and institutional fallouts, namely, overreliance on AI can erode lawyers’ critical judgment and even infringe rights (e.g., through opaque predictive systems), while data privacy and bias concerns multiply.
AI’s proper place in law must be conceived as supplemental, not determinative. Practitioners of law must supervise AI tools closely and test them under varied scenarios. We must adopt a citizen-centric, policy-aware stance: avoiding hyperbole on either side, critically examining what kinds of regulation, professional norms, and institutional practices could preserve justice and efficacy in the age of legal AI. AI’s development must align with constitutional principles of due process and fairness.
Pattern matching, not legal reasoning
Modern chatbots (e.g., ChatGPT, Google Gemini) are examples of what is called "narrow AI" systems. They are designed to perform specific tasks (such as generating text) by using statistical models trained on large datasets. Notably, they do not “reason” about the content in human terms. Rather, they use probability to predict the next word or sentence on a given prompt.
As one Thomson Reuters Institute analysis observed, “ChatGPT has no intelligence of its own; rather, it’s good at drafting language that sounds like a plausible response to a user’s prompt.” In effect, these models are sentence-completion engines built on patterns gleaned from vast text corpora. They are not rule-based engines or formal theorem provers. The result is that they lack the deliberate, logical processing that a regular legal analysis demands.
This limitation has been repeatedly demonstrated. LLMs fail standard math problems if details are tweaked; when extraneous information is added, their accuracy collapses. LLMs aren’t capable of formal reasoning – they are doing brittle pattern-matching. As Keith Porcaro notes, if LLMs are thrown wildly off course by extraneous or minor changes in wording, it’s hard to see how they can be the basis for reliable legal help. In conclusion, these tools lack the consistent logical framework that lawyers are habitual to use to interpret statutes, analogize cases, weigh competing legal principles, et cetera.
These gaps reflect the "narrow scope" of current AI: none are (yet) general AI that “understands” law. ChatGPT and similar variants are general-purpose LLMs, meaning, they were trained on broad internet text to handle any kind of conversation. This makes them protean, but also unfocused. In contrast, legal tasks require domain-specific knowledge: comprehensive databases of statutes, case law, and specialized terminology. Legal AI requires data sets, technology and human expertise all geared to the law that ChatGPT simply doesn’t have the training or domain expertise to be reliable on. A study finds that general AI chatbots hallucinate on legal queries 58–82% of the time, whereas specialized systems (even those built on LLMs) reduce errors substantially though still they are far from being perfect or even close.
Until true artificial general intelligence is not in existence, current narrow AI remains a probabilistic language tool, and not a legal scholar.
The demands of legal advice
Legal services are for sure not casual conversations. It demands highly structured, precise, jurisdiction-specific output(s). A practitioner of law answering a legal question is trained to cite current law(s) and tailor his/her advice to the exact factual matrix and jotting jurisdiction on the basis of the location of the client. For example, a contract clause valid in one state might not be unenforceable in another, and tax rules vary country by country. Even the interpretation of plain language depends on context and decades of case law in a specific jurisdiction. These subtleties are beyond what general chatbots can reliably grasp upon.
Generic LLMs are not designed for this task. They do not have explicit access to up-to-date legal databases or texts (also, they seldom explain their sources). In practice, we see this limitation. A study by Stanford’s Human-Centered AI Institute found that ChatGPT, Google PaLM, and Meta’s Llama (all general models) answered legal questions incorrectly in the majority of cases. In fact, the same study was cited by a New York Bar task force report, that these models were wrong at least 75 percent of the time on core legal rulings. These models were trained on “limited material published on the internet” and their outputs do not necessarily reflect authoritative law. They generate “the most likely combination of words” given the prompt, not the accurate legal rule. As the UK judiciary has warned, public AI chatbots “do not provide answers from authoritative databases” and may simply fabricate responses.
In contrast to this with legal-specific AI tools, tools built with legal data (often using retrieval-augmented generation, RAG) are far more reliable than ChatGPT alone. For instance, proprietary systems like Thomson Reuters’ and LexisNexis’ legal AI assistants cut hallucinations, but still gave incorrect information up to 17–34 percent of the time in benchmarks. Even 17 percent error in a legal citation is quite unacceptable when attorneys owe duties of competence and candor.
Moreover, domain-focused systems typically expose their source texts or citations (e.g. linking to cases or regulations) so users can verify them. General chatbots often give confident answers with no trace of evidence. They may list imaginary cases, incorrect statutes, or misinterpret relevant facts. All these fail a lawyer’s requirement for verifiable, precise guidance.
Another issue is that law is explicitly jurisdiction-bound. An AI tool trained on internet global data cannot automatically know or distinguish which version of a law applies locally, and also, if laws changed after its training cutoff. Suppose when an Indian user asks ChatGPT about, say a statutory right, the bot might respond with an outdated law or possibly a rule from another country’s context. Such a result is risky and often leads to confusion. Even seasoned lawyers rely on up-to-date regional databases and local expertise. An AI chatbot treats all jurisdictions as generic.
This reveals why legal certainty cannot come from a “one-size-fits-all” language model. Without rigorous safeguards, any errors in structure, terminology, or local law can have dire real consequences.
Training data, opacity, and hallucinations
One of many core technical problems is that AI chatbots are trained on datasets, which are opaque and incomplete, or to put it this way, no one knows whether the datasets used are complete. Models like ChatGPT have opaque training corpora scraped right out from the internet, which may include outdated or unreliable sources. These models have no explicit understanding of truth. They lack fact-checkers or access to live databases; they only know and understand patterns. The result is - chronic hallucination, meaning: confidently asserted falsehoods.
The UK’s AI guidance notes that chatbots often “make up fictitious cases, citations or quotes, or refer to legislation… that do not exist,” and can “provide incorrect or misleading information regarding the law.” Chief Justice Roberts cautioned lawyers in a 2023 report about “hallucinations” of AI, after the New York incident drew national attention.
The lesson is clear: AI-generated legal responses must be treated with caution as they can be confidently wrong.
Why is this so profound for law? Because unlike trivia, there is no room for guesswork in legal advice or services. A bot’s plausible-sounding answer might sound to a person to waive a right or miss a deadline. The most vulnerable clients (those who cannot afford lawyers) stand at the highest risk from AI misinformation. If someone relies on a chatbot that fabricates facts, the fallout could be the loss of justice. This risk is further compounded by the opacity of LLMs as they do not explain why they gave a certain answer. The technical “black box” means users cannot trace an AI legal recommendation back to the statutes or cases. Without transparency, errors propagate unchecked. In high-stakes legal contexts – from consumer disputes to criminal law – such blind trust in an AI system or chatbots would demean the rule of law.
The resistance of law: Consistency, ethics, and procedure
The legal system has built-in demands that clash with AI’s weaknesses. Law is a domain of doctrinal consistency. Courts expect coherence, that is: follow precedents, stable legal definitions, and no swinging interpretations. A lawyer typically unifies a messy set of facts under established legal doctrines, confirming each case’s outcome aligns with the system’s principles. Yet, AI chatbots have no internal commitment or setting to such expected coherence. Each query to a model is separate and stateless: the same question phrased differently might produce a different answer. This brittleness makes AI chatbots ill-suited for the coherence demanded by justice.
Alos, Law enforces ethical standards on practitioners. They are bound by codes of ethics, requiring competence, candor, confidentiality, and a duty of care to their clients. An AI system or chatbot is not a licensed professional and carries no ethical duties. If a chatbot produces an answer, who is accountable? Presumably, the lawyer who used it - which means the lawyer must verify every AI suggestion or face professional discipline. Bar associations worldwide have begun to clarify that lawyers cannot delegate their ethical obligations to machines. For example, ABA guidance urges lawyers using AI tools to maintain competence, to supervise outputs, and to warn clients of limitations. None of the lawyers’ rules allow AI to substitute for a lawyer’s professional judgment. The burden on attorneys thus increases: they must not only keep up with law, but also vet AI’s findings. Many fear this will erode fundamental skills like critical reasoning and issue-spotting. Even, as an AI finds relevant keywords, the lawyer’s job of discerning which facts are material remains crucial.
Procedurally, law relies on human discretion and institutional safeguards. Judges ask questions, clients have the right to be heard, attorneys negotiate or cross-examine. An AI chatbot cannot replicate these human procedural roles. For instance, the case of using automated sentencing algorithms have been criticized for lacking transparency and denying defendants the chance to contest how an outcome was reached. Any heavy reliance on AI in court would trigger serious due-process concerns. The prospect of an AI “judge’s assistant” making finding-of-fact or law-based decisions without an oversight might run up against constitutional guarantees (like the right to a fair trial and to counsel) as well as democratic principles of accountable governance. Opaque AI risk assessments can violate equal protection or due process if used uncritically. Although not yet widespread, AI in the judiciary is on EU regulators’ radar: the EU’s AI Act explicitly lists “AI systems used by a judicial authority… to assist… in applying the law” as high-risk, meaning, they will be tightly regulated.
In short, law’s institutional frameworks - both ethical codes and constitutional rights - strongly resist any system that cannot be fully explained and checked by humans.