Explainable AI Tools

When AI Bites Back: Lessons from Anthropic’s Legal Misstep In a striking twist, Anthropic - The AI company championing safety and reliability - found itself entangled in a legal controversy involving its own AI model, Claude.The situation escalated when a court filing submitted by Anthropic's legal team included a citation generated by Claude that contained incorrect metadata, such as the wrong title and author names, despite linking to a valid source. Anthropic's attorney acknowledged the error, attributing it to Claude's formatting process and a missed manual review. The attorney emphasized that the mistake was "embarrassing and unintentional," not a deliberate fabrication. This incident underscores the challenges of integrating AI tools into legal workflows, especially when the tools themselves are under scrutiny. Although this is an AI fabrication problem, but also more of a human-in-the-loop problem - in this case, didn't verify the information generated by AI. Key Takeaways: ✔️ AI Hallucinations Are Real and Risky: Even advanced AI models like Claude can produce plausible but inaccurate information, known as "hallucinations." In legal contexts, such errors can have serious consequences. ✔️ Human Oversight Is Crucial: Relying solely on AI for tasks like citation formatting without thorough human review can lead to mistakes that undermine credibility. ✔️ Transparency Builds Trust: Openly acknowledging and correcting errors, as Anthropic did, is essential for maintaining trust in both legal proceedings and AI technologies. ✔️ Develop Robust Verification Processes: Implementing multiple levels of review can help catch AI-generated errors before they become public issues. ✔️ Understand AI's Limitations: Recognizing that AI tools have limitations and can make mistakes is vital for their effective and responsible use. This case serves as a cautionary tale for all sectors integrating AI into their operations. As the Arabic proverb goes, "He died by the poison he made." It's a reminder that the tools we create can have unintended consequences if not used wisely. #AI #LegalTech #Anthropic #Claude #ArtificialIntelligence #EthicsInAI #LegalInnovation

China’s recent court ruling on AI “hallucinations” is quietly significant. In a 2025 Hangzhou Internet Court case, the first publicly reported civil decision on AI hallucination liability, a Chinese court declined to impose automatic responsibility on an AI provider for false outputs. Instead, it applied familiar tort principles: fault, causation, attribution, and demonstrable harm. No breach of duty, no attributable fault, and no proven loss meant no liability. Crucially, the court refused to treat the model’s statements as legally binding expressions of the provider’s will. Generative AI was framed as a probabilistic service, not an agent capable of intent, and not a defective product subject to strict liability. At the same time, providers remain subject to strict duties in relation to illegal and harmful content. This is not deregulation, but calibrated restraint. Ordinary inaccuracies are treated differently from governance failures. The ruling also suggests that “hallucination harms” are not uniform. Harmless errors, economic reliance losses, reputational damage, and safety-related harms raise different liability questions, and may justify different standards of care. Contrast this with Europe’s precautionary model under the AI Act and revised Product Liability Directive, which pushes towards heavier ex ante compliance. The proposed AI Liability Directive has been withdrawn, but its risk-shifting logic remains influential. The United States remains more fragmented, relying on existing doctrines and regulatory guidance, for now. Singapore focuses on governance frameworks and shared responsibility rather than punitive exposure. What is emerging is not a single global AI liability regime, but three broad approaches: China’s fault-based pragmatism, Europe’s precautionary regulation, and the US-Singapore model of adaptive governance. The real question is not whether AI will make mistakes. It will. The question is how societies choose to allocate responsibility when it does. As AI becomes embedded infrastructure, that choice will shape innovation and institutional trust alike.

What happens when a judge includes fake, AI-hallucinated cases in an actual opinion? Here’s an interesting AI hallucination issue that legal tech companies may soon need to confront. In Williams v. Capital One Bank, N.A., 2025 U.S. Dist. LEXIS 49256, U.S. District Judge Rudolph Contreras included AI-generated fake cases in his opinion—not as legal authority, but as cautionary examples of what happens when litigants rely on unverified AI outputs. Here's the excerpt: "Courts have recently seen increasing reliance on artificial intelligence in legal proceedings, leading to the use of nonexistent citations in court documents.  . . . For example, "Pettway v. American Savings & Loan Association, 197 F. Supp. 489 (N.D. Ala. 1961)" is not a case that exists. Id. at 4. While Williams v. Equifax Information Services, LLC is a case that exists, "560 F. Supp. 2d 903 (E.D. Va. 2008)" is the incorrect citation, . . ." Here’s the crux: By appearing in a published federal opinion, those hallucinated cases are now part of the official case opinion—and included in legal research databases (albeit not hyperlinked or independently accessible via traditional search) But if a lawyer uses Westlaw CoCounsel, Lexis Protege, or another AI-powered research assistant, is it possible those fake citations could be surfaced as legitimate due to their inclusion in the opinion? Lexis seems to have taken a first step by adding this disclaimer: “Notice: This decision contains references to invalid citations in the original text of the opinion. They are relevant to the decision and therefore have not been editorially corrected. Linking has been removed from those citations.” But when using RAG-powered legal research tools, how will they prevent these ghost citations from being misinterpreted as legitimate?

ChatGPT just got sued for practicing law without a license. We need a better term than "AI hallucination." A hallucination is a wrong fact. What happened here is far more dangerous. I'm calling it "synthetic conviction": an AI manufacturing belief and certainty where none is warranted, then handing the user the tools to act on it. Here's the story. A woman lost a disability benefits dispute, settled the case, and had it dismissed with prejudice. Done deal. A year later, she fed the settlement agreement into ChatGPT and asked if her lawyer had been gaslighting her. ChatGPT said yes. She fired her attorney. Then she used ChatGPT to draft motions, a pro se appearance, and eventually an entire new lawsuit. Over the next year she filed 65+ AI-generated motions and demands. Including a citation to a case that does not exist. The court rejected everything. Nippon Life then sued OpenAI for tortious interference, abuse of process, and unlicensed practice of law, seeking $10M in punitive damages. This lawsuit probably isn't going anywhere. The legal theories are a stretch. But the story is the real headline. A chatbot played lawyer, told a person what she wanted to hear, and removed every friction point between a grievance and a courtroom filing. If you're a CIO, GC, or compliance leader, here's what I'd recommend right now: 1. Update your acceptable use policies to explicitly address AI-generated legal, medical, and financial content. 2. Train your people on synthetic conviction, not just hallucinations. The danger isn't wrong facts. It's manufactured certainty. 3. Build AI governance that includes output accountability, not just input controls. 4. If you don't have an AI governance framework yet, that's the gap that gets you in a courtroom. Govern it or get surprised by it. #AIGovernance #LegalTech #GenerativeAI #TheCIOAttorney #SyntheticConviction

AI Has Officially Reached Queens Housing Court—It’s Not Just Big Law Anymore This week, in a courtroom in Queens, a landlord’s attorney filed an affirmation in an eviction case in which they cited SEVEN fabricated cases. Not just factually incorrect—but entirely fabricated. A hallucination. The likely culprit? ChatGPT. The Judge in the case is recommending sanctions against that attorney. That decision is here: https://lnkd.in/eWkevnYM This marks a watershed moment. Generative AI has permeated the daily grind of housing court—not in a corporate skyscraper or an Ivy-clad appellate brief, but in the small, high-volume, under-resourced courtrooms where people’s homes are on the line. AI has reached solo practitioners and mom-and-pop landlords. It’s here. We should not be surprised. Generative AI tools are fast, persuasive, and free. They feel like legal assistants, but without the oversight or training. And in a court system already strained by volume and inequality, the temptation to lean on them—especially for time-strapped or inexperienced lawyers—is enormous. But this incident is more than a digital footnote. It’s a harbinger. It shows us that AI isn’t just a tool for high-end firms; it’s already reshaping the front lines of justice. And unless we move quickly to educate, regulate, and integrate these technologies responsibly, we’ll see more hallucinated citations, more procedural chaos—and more harm to the very people the legal system is meant to protect. BAD LAWYERS WILL ALWAYS EXIST, BUT WITH AI, THEY WILL BE DANGEROUS. This moment calls for vigilance, not panic. Innovation, not rejection. We need AI literacy across the legal profession, especially in the spaces where access to justice is already fragile. Because in Queens Housing Court—and courts like it across the country—the future has already arrived. #LegalTech #AccessToJustice #AIandLaw #QueensHousingCourt #ChatGPT #LegalEthics #HousingJustice #LegalInnovation #TenantsRights

I've completed the second article in my series examining AI hallucinations in legal practice, expanding the analysis to 35 cases across six jurisdictions. "Entering the Hall of Hallucinations: AI, Law, and Global Verification Failures" examines how this phenomenon transcends jurisdictional boundaries, with consistent patterns in verification practices and judicial responses. The research identifies several key findings: - Courts worldwide have reached remarkably similar conclusions about AI hallucinations despite different legal traditions; - The verification failure rate has remained consistent (97% of cases) even as awareness of AI limitations has grown; - Neither experience level nor institutional context appears to correlate with verification practices; This convergence suggests we're facing universal challenges in AI integration that require coordinated responses from courts, regulators and educators. #LegalTech #AIinLaw #ComparativeLaw #LegalProfession #ProfessionalStandards

China just took an important first step on AI hallucination liability, and it is notably friendly to developers. The Hangzhou Internet Court ruled that an AI developer is not automatically responsible when a chatbot hallucinates, even where the system confidently invents facts and then offers compensation that sounds like a real promise. In this case, the model created a non‑existent campus of a real university, insisted it existed, then said it would pay 100,000 yuan if it was wrong and suggested the user sue. The user did sue for damages, but the claim was dismissed. Three points from the ruling are worth watching for anyone advising on AI: 1️⃣ First, the court treated AI‑generated content as a service rather than a product. That framing matters. It means there is no automatic, product‑style liability just because the output is wrong. The user has to show both that the developer was at fault in how the system was designed or operated and that the hallucination caused actual harm in real life. 2️⃣ Second, the AI’s words are not the company’s promises. The court said the system has no legal personhood, so it cannot make binding declarations of intent, and there was no evidence that the developer had authorised it to commit the company. The 100,000 yuan “offer” therefore had no contractual force. 3️⃣ Third, the court signalled that hallucinations, by themselves, are not treated as a high‑risk activity. It noted that developers have limited control over specific outputs and warned that imposing strict liability for every error could chill innovation. Under existing rules, providers must review and remove illegal or harmful content, but they are not expected to guarantee that every response is accurate. For in‑house counsel and policymakers, a few questions follow. 👀 What does duty of care look like when these systems are used in higher‑risk contexts such as health, finance or employment, where “mere” hallucinations can quickly become real‑world harm? How far can providers rely on disclaimers and “AI may be inaccurate” notices before courts start looking for more concrete safeguards in design, monitoring and escalation processes? And as organisations embed models deep into workflows and citizen‑facing services, where exactly does responsibility sit when users act on wrong, but not obviously illegal, AI output? This is one judgment and not the final word. But it offers an early glimpse of how Chinese courts may try to balance innovation with accountability, and it is a signal that anyone deploying AI tools into their business in or with China should be paying attention to.

ChatGPT hallucinations landed this law firm with $50,000 in sanctions. The Order is incredibly instructive - it's a fascinating narrative of how lawyers' processes are being exposed by the reality of AI. The details here matter as they go to the process issues: The underlying dispute was an evidentiary issue in a case over lead exposure - the judge in the case had ruled that certain evidence was inadmissible, and despite that ruling the defending counsel had repeatedly brought up this evidence to the jury (over repeated sustained objections and admonishments). On appeal, defending counsel brought a motion arguing that the ruling barring this evidence was in error - and it was at this point in the process that a hallucinated case citation appeared, in the form of an on-point Supreme Court case going directly to the issue at hand that was wholly invented by AI. It's worth stressing: This case was central authority on a key issue of admissibility - and despite this, apparently no one on the defending legal team ever tried to look it up and read it. It was introduced when one partner used ChatGPT in her contributions to the motion - the Order here implies that she didn't check any of the citations because she both didn't know that ChatGPT could make up cases, and she assumed that others on the team would check her work before it was filed. (It's worth noting that this attorney had been sanctioned before for submitting fake case citations from ChatGPT, so her contention that she didn't know it could make up cases is somewhat doubtful.) Obviously, no one ever did, in fact, check her work - the signing attorney apparently deferred to another partner in the firm's appellate practice for issues of law, and only read the motion as "final look-through." He explicitly states that he "was not - nor was [he] expected to, nor did [he] expect [himself] to - cite check over 58 cases." But SOMEONE should have done so - if there was ever one single attorney who was asked to cite check the brief, it isn't mentioned in the Order. The Order very much reads like everyone on the team was deferring to everyone else for the work of confirming the case citations, and no one was actually tasked with doing so. This was manifestly a process failure - as much as the offending attorneys here talk about how "disgusting" they find the use of AI in drafting, the real offense here seems to be that not one single person on the legal team ever bothered to look up a case representing central authority on a key legal issue. In addition to the $50K in sanctions imposed on the firm, at least one partner lost her job and had to pay $10,000 for her error. Check your citations.

To all of you getting legal advice from ChatGPT, your future litigation adversaries thank you. Basically all of your prompts are discoverable. In a recent ruling, Judge Rakoff (SDNY) held that documents generated using an AI tool were not protected by attorney-client privilege or the work-product doctrine, identifying several independent reasons why privilege could not attach. First, the materials were not communications between a client and an attorney. Attorney-client privilege applies only to confidential communications exchanged for the purpose of obtaining legal advice from a lawyer. Here, the defendant created the documents by interacting with an AI system—a third-party technology provider that is not a lawyer, does not provide legal advice, and owes no professional duties of confidentiality or loyalty. Communications with an AI tool therefore fall outside the core scope of the privilege. Second, the documents lacked a reasonable expectation of confidentiality. The AI platform’s terms of service expressly disclaimed confidentiality and allowed the provider to retain or use user inputs and outputs. Judge Rakoff emphasized that sharing information with a third party under such conditions defeats privilege, because confidentiality is a prerequisite to protection. Third, Judge Rakoff rejected the argument that the documents became privileged when they were later sent to counsel. Privilege does not attach retroactively: materials that are non-privileged when created do not become privileged simply because a party later transmits them to an attorney. This principle is well established in federal law and applied squarely to the AI-generated materials. Fourth, the work-product doctrine did not apply. Work product protects materials prepared by or at the direction of counsel in anticipation of litigation. The AI documents were created independently by the defendant, not at counsel’s request or under counsel’s supervision, and they did not reflect an attorney’s mental impressions, legal strategy, or litigation planning. Finally, the court noted that the AI system itself disclaimed providing legal advice, further undermining any claim that the documents were part of a protected legal consultation. Taken together, Judge Rakoff’s ruling makes clear that using consumer AI tools to generate documents—without attorney involvement, confidentiality assurances, or counsel’s direction—will generally defeat claims of privilege. Screenshots of the relevant reasoning. https://lnkd.in/dUN2acy2

TO ALL CALIFORNIA ATTORNEYS OF RECORD: TAKE NOTICE THAT THE CALIFORNIA COURT OF APPEAL IS NOT PLAYING AROUND WHEN IT COMES TO THE USE OF ARTIFICIAL INTELLIGENCE! In Noland v. Land of the Free, the first published California opinion involving attorney use of AI-hallucinated quotes and case citations, the Second District Court of Appeal imposed a $10,000(!) monetary sanction on a California attorney who filed an appellate brief and reply littered with inaccurate, AI-generated citations. The Court also directed the attorney to serve a copy of the Court's opinion on his client and directed its clerk to serve a copy on the State Bar. What's more, the Court made clear that the attorney was getting off light, imposing "a conservative sanction" because counsel had represented that his errors were unintentional (he represented that he was unaware that LLMs could hallucinate quotes and citations when he had incorporated their outputs into the briefs) and because he expressed remorse for his actions. Should future attorneys find themselves in a similar spot, they should not expect similar leniency. This is particularly true given that there is now published case law making abundantly clear that using AI in the preparation of materials filed with the court directly implicates basic duties attorneys owe to their clients and the court: "Simply stated, no brief, pleading, motion, or any other paper filed in any court should contain any citations - whether provided by generative AI or any other source - that the attorney responsible for submitting the pleading has not personally read and verified... To state the obvious, it is a fundamental duty of attorneys to read the legal authorities they cite in appellate briefs or any other court filings to determine that the authorities stand for the propositions for which they are cited."