The general counsel for the Oregon State Bar said fabricated cases and citations have become more common among lawyers and people representing themselves.
Lawyer Caught Using AI While Explaining to Court Why He Used AI
The attorney not only submitted AI-generated fake citations in a brief for his clients, but also included “multiple new AI-hallucinated citations and quotations” in the process of opposing a motion for sanctions.
Hallucination-Free? Assessing the Reliability of Leading AI Legal Research Tools
Legal practice has witnessed a sharp rise in products incorporating artificial intelligence (AI). Such tools are designed to assist with a wide range of core legal tasks, from search and summarization of caselaw to document drafting. But the large language models used in these tools are prone to "hallucinate," or make up false information, making their use risky in high-stakes domains. Recently, certain legal research providers have touted methods such as retrieval-augmented generation (RAG) as "eliminating" (Casetext, 2023) or "avoid[ing]" hallucinations (Thomson Reuters, 2023), or guaranteeing "hallucination-free" legal citations (LexisNexis, 2023). Because of the closed nature of these systems, systematically assessing these claims is challenging. In this article, we design and report on the first preregistered empirical evaluation of AI-driven legal research tools. We demonstrate that the providers' claims are overstated. While hallucinations are reduced relative to general-purpose chatbots (GPT-4), we find that the AI research tools made by LexisNexis (Lexis+ AI) and Thomson Reuters (Westlaw AI-Assisted Research and Ask Practical Law AI) each hallucinate between 17% and 33% of the time. We also document substantial differences between systems in responsiveness and accuracy. Our article makes four key contributions. It is the first to assess and report the performance of RAG-based proprietary legal AI tools. Second, it introduces a comprehensive, preregistered dataset for identifying and understanding vulnerabilities in these systems. Third, it proposes a clear typology for differentiating between hallucinations and accurate legal responses. Last, it provides evidence to inform the responsibilities of legal professionals in supervising and verifying AI outputs, which remains a central open question for the responsible integration of AI into law.
The MyPillow guy’s lawyers got fined for using false AI-generated citations.
The attorneys defending MyPillow founder Mike Lindell in a defamation case (which he lost) were ordered to pay $3,000 each for putting AI-generated misquotes and “citations of cases that do not exist” in a brief submitted in February, as reported by ArsTechnica. More and more lawyers are getting caught — and punished — for including AI hallucinations in their work, and this trend will likely only continue to grow. [Link: Why do lawyers keep using ChatGPT? | https://www.theverge.com/policy/677373/lawyers-chatgpt-hallucinations-ai | The Verge]
California Enacts SB 574, Nation’s First Law Governing Attorneys’ Use of Generative Artificial Intelligence | Insights | Ropes & Gray LLP
California's SB 574, signed Sept. 30, 2026 and effective Jan. 1, 2027, is the first state law setting standards for attorneys' use of generative AI.
18 Lawyers Caught Using AI Explain Why They Did It
Lawyers blame IT, family emergencies, their own poor judgment, their assistants, illness, and more.
Lawyers Caught Citing AI-Hallucinated Cases Call It a 'Cautionary Tale'
The attorneys filed court documents referencing eight non-existent cases, then admitted it was a "hallucination" by an AI tool.
AI for Law Firms: What the 8am Legal Industry Report Tells Us About AI Use
New data from 1,300+ legal professionals shows generative AI use has more than doubled year over year. The real question now: How should law firms structure governance and integration?
Fraudulent citations, blamed on AI hallucinations, are becoming more common in research papers
“Fabricated” citations that do not reference real academic papers are spreading in the literature, polluting the public record of science, a new study found
Judge Learns Lawyers on Both Sides of Case Used AI, Cancels Trial, Kicks Everyone Off the Case
When two AIs argue against each other, the legal system loses.
Judge slams lawyers for ‘bogus AI-generated research’
The law firms involved must pay $31,000.
Does AI Assistance Enhance or Erode Expertise? Evidence from a Three-Month Field Experiment in Patent Drafting
Whether AI assistance builds or erodes professional expertise is unsettled. In a pre-registered three-month randomized controlled trial, we gave 133 practicing patent lawyers at eleven U.S. intellectual property law firms access to a custom AI drafting assistant and measured both their performance while using AI and their professional judgment afterward without it. All work was scored by blinded expert patent attorneys. Paralleling findings from other white-collar domains, AI access raised the quality of work delivered on benchmark patent drafting tasks at 10 days (0.34 SD, p = 0.03) and 90 days (0.38 SD, p = 0.01), with larger gains among junior lawyers. After three months, all subjects redlined an existing patent application without AI, a core task of patent practice requiring expert judgment. Treated lawyers outperformed controls by 0.32 SD (p = 0.04), but this advantage was concentrated entirely among senior lawyers (0.45 SD, p = 0.02). Junior lawyers showed no average gain; their scores instead bifurcated, with sharply fewer mediocre scores offset by more poor and more good ones. The largest gains from AI thus accrued to the lawyers who retained the least. Foundational expertise may be a prerequisite for extracting durable skill from AI-assisted practice.
AI Hallucination Cases Database – Damien Charlotin
The most comprehensive database of AI hallucination cases in law: legal decisions from courts worldwide, searchable by country, party, AI tool, and outcome. Updated daily.
False authorship: an explorative case study around an AI-generated article published under my name
The proliferation of generative artificial intelligence (AI) has facilitated the creation and publication of fraudulent scientific articles, often in predatory journals. This study investigates the extent of AI-generated content in the Global International Journal of Innovative Research (GIJIR), where a fabricated article was falsely attributed to me.
Automated Justice: Issues, Benefits and Risks in the Use of Artificial Intelligence and Its Algorithms in Access to Justice and Law Enforcement
The use of artificial intelligenceArtificial Intelligence (AI) (AI) in the field of law has generated many hopes. Some have seen it as a way of relieving courts’ congestion, facilitating investigations, and making sentences for certain offences more consistent—and therefore fairer. But while it is true that the work of investigators and judges can be facilitated by these tools, particularly in terms of finding evidenceEvidence during the investigative process, or preparing legal summaries, the panorama of current uses is far from rosy, as it often clashes with the reality of field usage and raises serious questions regarding human rightsHuman rights. This chapter will use the RobodebtRobodebt Case to explore some of the problems with introducing automationAutomation into legal systems with little human oversight. AI—especially if it is poorly designed—has biases in its data and learning pathways which need to be corrected. The infrastructures that carry these tools may fail, introducing novel bias. All these elements are poorly understood by the legal world and can lead to misuse. In this context, there is a need to identify both the users of AIArtificial Intelligence (AI) in the area of law and the uses made of it, as well as a need for transparencyTransparency, the rules and contours of which have yet to be established.
Automated Justice: Issues, Benefits and Risks in the Use of Artificial Intelligence and Its Algorithms in Access to Justice and Law Enforcement
The use of artificial intelligenceArtificial Intelligence (AI) (AI) in the field of law has generated many hopes. Some have seen it as a way of relieving courts’ congestion, facilitating investigations, and making sentences for certain offences more consistent—and therefore fairer. But while it is true that the work of investigators and judges can be facilitated by these tools, particularly in terms of finding evidenceEvidence during the investigative process, or preparing legal summaries, the panorama of current uses is far from rosy, as it often clashes with the reality of field usage and raises serious questions regarding human rightsHuman rights. This chapter will use the RobodebtRobodebt Case to explore some of the problems with introducing automationAutomation into legal systems with little human oversight. AI—especially if it is poorly designed—has biases in its data and learning pathways which need to be corrected. The infrastructures that carry these tools may fail, introducing novel bias. All these elements are poorly understood by the legal world and can lead to misuse. In this context, there is a need to identify both the users of AIArtificial Intelligence (AI) in the area of law and the uses made of it, as well as a need for transparencyTransparency, the rules and contours of which have yet to be established.
We Talked to a Writer Accused of Publishing An AI-Generated Essay in The New York Times
She was accused of publishing AI slop in The New York Times. She says she didn't use AI to generate content — but did use AI to get published.
