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Lawyers can use generative AI in legal work, but they remain responsible for its output. Existing duties of competence, confidentiality, diligence, candor, supervision, and reasonable fees apply when a lawyer uses an AI system; the technology does not take over professional judgment. Before relying on AI-generated work, a lawyer needs to understand the tool and task, protect client information, and independently check material claims and authorities. The precise obligations depend on the governing jurisdiction, the client relationship, and any court order or agreement.
What counts as an AI hallucination in legal work?
A hallucination is an AI-generated claim that is false or unsupported, presented as though it were reliable. In law, the risk is broader than a case name that does not exist. A system might invent a decision, describe a real decision inaccurately, attach a made-up quotation or parenthetical to a genuine citation, or give a confident answer that the cited material does not support.
That differs from an incomplete response. A model can omit relevant authority or fail to answer fully without fabricating a fact. The distinction matters when interpreting studies: a benchmark’s reported hallucination rate depends on what researchers count as a hallucination and what questions they ask. A plausible-looking citation, link, or quotation is not proof that the material exists or says what the output claims.
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There is no general prohibition on using generative AI in legal practice in the guidance discussed here. The central professional-responsibility point is that using a tool does not shift accountability away from the lawyer. The American Bar Association’s Formal Opinion 512 warns: “Because GAI tools are subject to mistakes, lawyers’ uncritical reliance on content created by a GAI tool can result in inaccurate legal advice to clients or misleading representations to courts and third parties.” The opinion applies the ABA Model Rules as amended through August 2023; it is guidance about those model rules, not automatically a binding rule in every state. Read ABA Formal Opinion 512.
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Competence includes having a reasonable understanding of a tool’s capabilities and limitations, and keeping that understanding current as the technology changes. A lawyer need not become an AI engineer, but should know enough about the system and intended use to decide whether its output can be relied on, what review it needs, and whether the use presents confidentiality or other ethical concerns. A feature that worked adequately for one task or version does not establish that it will do so for another.
Opinion 512 says the appropriate degree of independent verification varies with the tool and task. A brainstorming aid may call for a different review process from a system used to summarize evidence, advise a client, draft a motion, or identify controlling authority. In every case, the lawyer must exercise independent professional judgment and remains responsible for the work product.
How to verify AI-generated legal citations and claims
Treat AI output as a draft or research lead, not as authority. The following operational workflow applies the ABA and Florida guidance; it is a practical synthesis, not a checklist prescribed identically by every jurisdiction.
- Identify the claims that matter. Mark every legal proposition, factual assertion, quotation, citation, and procedural claim that could affect advice, a filing, a negotiation, or a client decision. Prioritize claims whose error could cause a missed deadline, incorrect advice, or a misleading representation.
- Locate each authority independently. Search for the cited case, statute, rule, or other primary source in a reliable legal source rather than relying on the AI’s citation string or linked excerpt. Confirm that the authority exists and that the citation identifies the intended document.
- Read the relevant material in context. Check the actual opinion or text, including the passages surrounding the cited proposition. A real case can be mischaracterized, taken out of context, or cited for a holding it does not support.
- Check quotations and pin cites word for word. Compare each quotation with the source and confirm that the pinpoint citation leads to the quoted language. Do not accept a quotation merely because it sounds like judicial writing or appears beside a genuine case name.
- Confirm procedural posture and subsequent status. Make sure the source is the relevant decision and that its procedural history or later treatment does not undermine the way it is being used. Confirm that the authority remains usable for the proposition in the relevant court and jurisdiction.
- Verify material facts against the record or other reliable evidence. A model’s factual summary is not a substitute for checking the underlying documents. Resolve discrepancies rather than carrying unsupported details into advice or a filing.
- Have a responsible lawyer review the finished work. Review the actual client-facing or court-facing document, not just the AI draft or a sample of its output. The reviewer should be able to explain and defend the important assertions independently.
If a system cannot provide sources that can be checked, that limits its usefulness for authority-dependent tasks; it does not lower the lawyer’s verification obligation. Source links and retrieval features can make checking easier, but a link does not establish that a source supports the generated statement.
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Can I put client information into an AI tool?
Do not decide based on a product label such as “private” or “enterprise.” The relevant question is what happens to the information in the particular system, under its current terms and configuration, and whether that handling is consistent with confidentiality duties, client expectations, and applicable rules. ABA Opinion 512 identifies disclosure of client information and unauthorized access as risks. Florida Bar Opinion 24-1 specifically tells lawyers to investigate a program’s retention, sharing, and self-learning policies. It notes that an in-house system may mitigate concerns where no third-party disclosure occurs, but that does not make every in-house or commercial setup automatically appropriate.
Before submitting identifiable client information, establish what information the tool receives and what happens afterward. Ask who can access prompts and uploads, whether they are retained, whether they are used to improve or train models, what deletion controls apply, whether the firm has approved the service, and how the data flow fits the client’s instructions and the governing rules. These are practical investigation questions drawn from the cited guidance, not a claim that every jurisdiction mandates a particular vendor setting.
When the system’s handling is uncertain, avoid entering identifiable or sensitive client material until the uncertainty is resolved. Depending on the task and applicable requirements, a lawyer might use non-identifying information or an approved environment instead. Removing a name alone may not be enough if the remaining details could identify the client or matter. The lawyer must assess the actual information and configuration, rather than assume that a simple substitution eliminates the risk.
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Do lawyers have to tell clients they used AI?
The ABA opinion does not create a universal requirement to disclose every AI use to every client. It discusses consultation when the use affects a significant decision in the representation or when a client would reasonably want to know whether the lawyer is exercising independent judgment or deferring to AI. Whether consultation or consent is needed turns on the matter, the nature and consequences of the use, client expectations, engagement terms, and the governing jurisdiction’s rules.
The New York City Bar’s Formal Opinion 2024-5 addresses generative AI in New York practice and includes consultation, supervision, candor, and reasonable fees among the issues lawyers should consider. It is not a nationwide rule. Lawyers should check applicable state authority and any matter-specific obligations before making a categorical statement about disclosure or consent. Read New York City Bar Formal Opinion 2024-5.
For firm leaders, a written policy can make the decision consistent without pretending every use presents the same issue. It can identify approved tools and uses, who decides when client consultation is appropriate, how confidentiality is assessed, what level of source checking is required, when uncertain outputs must be escalated, and who signs off on final work. Supervision should address the actual task and the lawyer’s role, not merely whether an employee or contractor clicked an AI feature.
What do studies say about legal AI hallucinations?
Two 2024 studies illustrate why no single percentage should be treated as an error rate for all legal AI. Their systems, questions, and definitions differ, so the results are not a head-to-head product comparison or a prediction of how a current version will perform on a lawyer’s task.
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| Study and tested task | Reported result | How to interpret it |
|---|---|---|
| Stanford RegLab and coauthors, 2024, preregistered evaluation of tested LexisNexis and Thomson Reuters legal research tools on the study’s query set | Hallucinations were reported between 17% and 33% of the time across the tested tools. | The range applies to the evaluated tools and query set, not every product, task, or later version. The study also distinguished hallucinations from incomplete answers. Read the study. |
| Yale Law School and Stanford coauthors, 2024, tested models answering specific, verifiable questions about randomly selected federal court cases | The study reported hallucination rates of 58% for ChatGPT-4 and 88% for Llama 2 on that task. | This was a different task and evaluation from the legal research-tool study; the percentages should not be used to rank those systems against the other study’s tools or to predict performance on other legal work. Read the study. |
The practical conclusion is not that a particular system has a fixed failure rate. It is that meaningful error risk persisted in the evaluated settings, varied across systems, and must be managed through task-specific review. Product versions and use cases change; a published benchmark describes what researchers tested, not a guarantee about a lawyer’s current workflow.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Can a lawyer be sanctioned for AI hallucinations?
Yes. A lawyer may face consequences when inaccurate AI-generated material is submitted or communicated without adequate checking, though an error does not automatically produce sanctions. In Mata v. Avianca, a 2023 federal court order described a filing that cited nonexistent decisions and included fabricated quotations generated with ChatGPT. After the court and opposing counsel questioned the authorities, the lawyers failed to investigate adequately. The Southern District of New York ordered a $5,000 payment to the court and required corrective letters, attaching the sanctions order and related materials, to the client and judges whose names had been falsely used. Read the June 22, 2023 order.
Judge P. Kevin Castel wrote: “But existing rules impose a gatekeeping role on attorneys to ensure the accuracy of their filings.” The order did not establish a categorical ban on AI; it stated that there is nothing inherently improper about using a reliable AI tool for assistance. The lesson is that lawyers must check what they submit and respond responsibly when a material problem is raised or discovered.
If questionable authority or a material AI-generated error reaches a client, opposing counsel, or a court, stop relying on it, verify the issue promptly, and take the corrective steps required by the applicable rules and circumstances. Preserve a clear record of the review and correction. Do not repeat or defend a disputed citation before checking the underlying source.
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How to choose a tool and set firm safeguards
Tool selection is not just a question of whether an answer sounds polished. Compare the deployment and workflow on the factors that affect both risk and the effort required to manage it.
- Confidentiality and data handling: Determine retention, sharing, access, and whether prompts or uploads are used for training or model improvement. Evaluate the actual configuration and terms rather than relying on a marketing description.
- Task performance and source quality: Consider whether the system is being used for research, summarization, drafting, or another task; whether it supplies sources; and whether those sources can be independently checked. A source link is a starting point for verification, not a substitute for it.
- Human review burden: Estimate how much work is needed to validate the output, including factual assertions, quotations, citations, and jurisdiction-specific propositions. If the output cannot be checked efficiently, it may not be suitable for a high-stakes task.
- Firm controls and auditability: Consider who may use the system, whether the firm can manage access, and whether a reviewer can understand what was submitted and what was produced. Set escalation and final-approval responsibilities.
- Client and jurisdiction constraints: Check engagement terms, client instructions, court orders, local rules, and applicable ethics authority before using the system in a matter.
A firm’s policy should make clear that a lawyer—not a tool—owns the final judgment. ABA Opinion 512 says lawyers should develop an ongoing understanding of GAI and maintain practices for appropriate verification. Florida’s opinion similarly emphasizes responsibility for work product. Neither an enterprise setting nor legal-domain training should be treated as proof that hallucinations or confidentiality risks have disappeared.
Billing for AI-assisted legal work
AI use does not justify billing clients for time that was not spent as though it were lawyer time actually worked. The ABA and Florida guidance address reasonable fees and costs, so firms should make sure invoices accurately reflect the services performed and comply with the engagement agreement and applicable rules. If AI changes the time or method involved in a task, the fee still needs an appropriate basis; automation does not turn saved time into time spent.
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Which rules govern your practice?
There is no single AI ethics opinion that settles every lawyer’s obligations everywhere. ABA Formal Opinion 512 interprets the ABA Model Rules, and its analysis is not itself a binding state rule. Florida Opinion 24-1 is advisory and nonbinding. The New York City Bar opinion addresses New York practice. State rules, court orders, client agreements, and the facts of a particular matter can change the answer.
Before adopting a categorical policy on disclosure, consent, confidentiality, or permissible use, identify the jurisdiction governing the work and consult its current authority. Also review the specific tool’s current terms and configuration: data practices can change, and general ethics guidance cannot establish what a particular system retains or shares.
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