When AI Mistakes Collide With High Stakes Court Battles
Aaron Greenspan, a former Tesla short seller and legal researcher, claims that a San Francisco judge allowed AI errors to influence a key motion. Greenspan alleges that multiple citation mistakes in a November ruling undermined his defamation and securities fraud lawsuit against Elon Musk. These errors, he says, appear to be the product of AI-generated content that misrepresented legal sources.
Greenspan argues that the combined mistakes handed the case to Musk, one of the wealthiest individuals in the world. He pointed out at least one major error, which the judge later amended in the ruling. However, Greenspan believes additional inaccuracies remain, potentially affecting the outcome of his case significantly.
The accusations highlight growing concerns over the use of AI in the legal system, particularly in judicial decision making. While AI tools can assist judges in summarizing motions or drafting routine orders, they also generate content that may be misleading or false. Greenspan’s case raises questions about the reliability of AI-assisted rulings in high-stakes litigation.
San Francisco’s policy allows limited AI use by judges and staff, provided humans review the results for accuracy. The policy does not clearly define what constitutes a substantial portion of AI-generated work, leaving room for interpretation and error. Greenspan’s allegations suggest that even minor reliance on AI can have outsized consequences in legal proceedings.
This case also reflects a broader tension between technology and judicial integrity, especially as courts adopt AI for research and administrative tasks. Experts warn that AI hallucinations, including misquoted cases or fabricated citations, can quietly influence judgments without immediate detection. Public confidence in the judicial system could be undermined if such errors become more frequent or visible.
Greenspan’s motion asking the judge to reconsider the order frames the issue as both a legal and technological challenge. The dispute illustrates the potential hazards of integrating AI into courtrooms without rigorous safeguards. The outcome of this motion could set a precedent for how AI errors are treated in future judicial decisions.
How Misquotes and Fabrications May Have Shifted the Ruling
Greenspan highlighted a November court ruling that included multiple apparent errors, which he says were likely generated by AI. One major issue involved the citation of Jones v. Goodman, a 2020 California appellate decision. The ruling cited it as supporting the defense, but the appellate court had actually decided the opposite way.
Additional mistakes included references to non-existent pages, fabricated quotations, and invalid citations that had no grounding in the original legal documents. Greenspan argued that these errors misrepresented the law and effectively strengthened Musk’s position in the defamation and securities fraud case. Even after the judge amended one major error, Greenspan believes other inaccuracies remain, potentially influencing the legal outcome unfairly.
The misquotes were particularly concerning because they mischaracterized procedural disputes, including which party filed crucial documents first. By treating the losing side’s arguments as authoritative, the order created a distorted legal narrative in favor of Musk. Greenspan claims this type of AI error amplified the imbalance in a case already tilted by resources and influence.
In some instances, the ruling included quotations that did not appear in the original cases, making it difficult for Greenspan to challenge the order. The errors were subtle, meaning they could easily be mistaken for conventional human oversight rather than machine-generated hallucinations. This subtlety makes it challenging to detect AI mistakes without meticulous review of every citation and quotation.
Experts like Joe Patrice have noted that AI often partially “screws up,” producing mistakes that are worse than obvious errors because they appear plausible on the surface. Unlike a clear fabrication, these partial errors are harder to spot, especially in complex legal arguments. Greenspan’s motion emphasizes that the consequences of even small AI hallucinations can be significant in high-stakes litigation.
The amended order corrected the Jones v. Goodman citation but did not address other alleged inaccuracies in the ruling. Greenspan maintains that these remaining errors continue to favor Musk, affecting both procedural and substantive aspects of the case. He argues that unless the court thoroughly reviews the order, the motion’s outcome may be fundamentally flawed.
Invalid references and omissions compound the problem because they mislead the court about precedent and legal reasoning. By presenting incomplete or incorrect information, AI errors can subtly steer judicial interpretation in unintended directions. Greenspan contends that this undermines both the fairness of the case and the integrity of the judicial process.
The alleged AI mistakes also illustrate how generative technology can create convincing but inaccurate legal content. Even routine reliance on AI tools, if not carefully monitored, can introduce errors that are difficult for litigants to anticipate. Greenspan’s allegations raise critical questions about accountability when AI contributes to judicial documents.
Ultimately, Greenspan’s claims suggest that AI-generated errors may have had a tangible effect on the denial of his motion. The case highlights the need for rigorous human oversight to ensure machine-generated content does not distort legal outcomes. Courts may face increasing pressure to establish stricter review protocols as AI use becomes more common.
How Artificial Intelligence Is Reshaping Courtroom Workflows
Judges across the United States are increasingly experimenting with AI tools like ChatGPT, Westlaw Precision, and Gemini to assist with research and drafting. These platforms can summarize complex motions, identify relevant case law, and streamline routine administrative tasks. The technology promises efficiency but also introduces the risk of subtle or obvious errors if human oversight is insufficient.
San Francisco Superior Court allows judges and staff to use AI tools under specific conditions, emphasizing that humans must review outputs carefully. The policy requires disclosure only when machine-generated content constitutes a substantial portion of work, though the definition of substantial remains unclear. Greenspan’s allegations illustrate how gaps in policy interpretation can create significant consequences for litigants when errors occur.
California’s broader Judicial Council guidelines, effective this year, mandate that any state court intending to implement AI must create an official AI usage policy. Courts are instructed to take reasonable steps to verify machine-generated content and correct errors when discovered. The guidelines also explicitly warn against AI hallucinations and stress the importance of maintaining accuracy in legal documents.
AI hallucinations are particularly problematic because they can produce content that appears plausible but is legally incorrect or entirely fabricated. Judges relying on AI-generated drafts or summaries might inadvertently incorporate these errors into official rulings. Even minor inaccuracies, such as misquoted cases or missing pages, can affect legal arguments and outcomes in high-stakes litigation.
Some tools, like Westlaw Precision, are designed specifically for legal research and have built-in mechanisms to minimize errors, yet they still require human verification. ChatGPT and Gemini, by contrast, are general-purpose language models that can generate hallucinations even when the underlying information is partially correct. This distinction emphasizes the importance of understanding each tool’s limitations before relying on it in a courtroom setting.
San Francisco’s AI policy permits limited AI usage but does not outline penalties for mistakes, leaving accountability largely dependent on individual judges and staffers. Critics argue that this creates a grey area where errors may persist without formal consequences. Greenspan’s case underscores the potential consequences when AI-assisted errors intersect with high-profile, high-stakes litigation.
The Judicial Council also encourages courts to implement safeguards for staff using AI, including verification protocols and iterative review processes. These steps are intended to catch inaccuracies before they reach official filings or rulings. Experts caution, however, that the sheer volume of AI-generated content in some courtrooms may overwhelm human reviewers, increasing the likelihood of errors slipping through.
Despite the benefits of AI, legal scholars emphasize that human judgment remains essential for evaluating context, precedent, and procedural nuances. AI tools can assist, but they cannot replace the interpretive and ethical responsibilities of judges and legal staff. Greenspan’s allegations highlight the tension between adopting AI for efficiency and ensuring absolute accuracy in judicial decisions.
As AI becomes more integrated into legal practice, courts face a balancing act between leveraging technology and preventing errors that could undermine public trust. Policies and guidelines provide a framework, but their effectiveness depends on strict adherence and careful oversight. The Greenspan case serves as a cautionary tale for the legal system as AI use expands nationwide.
What Legal Experts Warn About AI Mistakes in Court
Joe Patrice, an attorney and legal commentator, noted that AI errors often appear subtle but can have outsized consequences in court documents. He explained that AI rarely produces completely obvious mistakes, instead creating half-accurate content that seems plausible at first glance. These errors can mislead judges and attorneys, making it difficult to detect hallucinations without meticulous review.
US Magistrate Judge Allison Goddard emphasized that AI mistakes in judicial settings are particularly troubling because of public scrutiny and the high stakes involved. Even small hallucinations can erode confidence in court decisions, she warned. Human oversight is critical to ensure that AI outputs do not distort legal analysis or outcomes.
Damien Charlotin, a legal researcher, maintains a database tracking over 600 confirmed AI errors in filings worldwide since 2023. More than 400 of these errors occurred in the United States, demonstrating the widespread nature of the problem. Charlotin’s data reveals that both self-represented litigants and professional lawyers contribute to these mistakes, though judges have also been implicated in several cases.
Eugene Volokh, a UCLA law professor, noted that many AI errors go unreported or unnoticed, making the problem likely larger than existing records suggest. He estimates that for every error officially documented, multiple others remain hidden in court documents. These hidden mistakes can influence legal interpretation even if they are never formally challenged or corrected.
In one extreme example, a Los Angeles attorney filed an appeal where 21 of 23 quotations in the opening brief were fabricated using ChatGPT. The lawyer admitted to not reviewing the AI-enhanced brief carefully before submission, leading to a US$10,000 fine. The appellate court called attention to the darker consequences of AI hallucinations in legal practice and warned against their careless use.
Patrice explained that modern AI errors are more insidious than earlier outright fabrications, because they manipulate real text while subtly altering meaning. In Greenspan’s case, misquoted passages came from genuine legal opinions but were misrepresented in context. AI’s inability to understand narrative flow in legal reasoning increases the risk that machine-generated errors appear credible.
Goddard stressed that courts have no margin for error when public trust is at stake, making AI oversight essential. The consequences of hallucinations in high-profile litigation extend beyond individual cases, potentially undermining the judiciary’s reputation. Judges and court staff must adopt rigorous review protocols to prevent AI-generated mistakes from influencing rulings.
Volokh noted that recent filings reveal a new pattern: real cases are quoted inaccurately, producing believable but misleading legal content. This subtlety complicates detection because the cases exist but are misinterpreted or misrepresented. Courts now face the dual challenge of correcting errors while preventing AI from introducing new inaccuracies into legal precedent.
Charlotin and other experts emphasize that AI use in law is growing rapidly, increasing the stakes for courts and litigants alike. Without robust safeguards, hallucinations could become embedded in judicial decisions, affecting outcomes and public confidence. Greenspan’s allegations exemplify the risks of relying on AI without thorough human verification in high-stakes legal proceedings.
When Efficiency and Accuracy Collide in AI Assisted Courts
Greenspan’s allegations underscore the potential consequences of AI errors in legal rulings, highlighting how even subtle mistakes can significantly influence outcomes. The case illustrates the challenges courts face when integrating technology into judicial processes. Errors in citations and quotations can alter legal reasoning, with implications far beyond individual cases.
While AI tools promise efficiency, their adoption carries inherent risks, particularly when hallucinations go undetected by human reviewers. Judges must navigate the tension between speed and accuracy, ensuring that technological assistance does not compromise fairness. Public confidence in the judicial system hinges on maintaining meticulous standards despite the allure of AI efficiency.
The Greenspan case exemplifies the stakes when AI-generated mistakes intersect with high-profile litigation involving powerful individuals. Minor inaccuracies in rulings may disproportionately benefit wealthier parties, raising questions about equity and access to justice. Courts must develop robust verification systems to prevent machine-generated content from distorting judicial decisions.
Experts warn that AI hallucinations are often insidious, subtly altering meaning while appearing credible, which makes them harder to detect than obvious errors. Legal scholars stress that without rigorous oversight, these errors could embed themselves in precedent, affecting not only current cases but future interpretations of the law. Courts will need to prioritize transparency and review protocols to safeguard both accuracy and legitimacy.
As AI adoption grows, judicial systems must strike a balance between leveraging technology for efficiency and preserving integrity, trust, and procedural fairness. Policies alone cannot prevent errors; human scrutiny and accountability remain critical components of responsible AI use in courts. Greenspan’s allegations serve as a warning about the potential consequences of underestimating these challenges.
Ultimately, the integration of AI in legal proceedings is not inherently harmful, but its use demands caution, oversight, and continuous evaluation. Courts must ensure that efficiency gains never come at the cost of fairness, accuracy, or public trust. The ongoing debate around Greenspan’s case highlights the pressing need for careful management of AI in high-stakes judicial settings.
