7 posts tagged with "Law"

In re: Turner, Disbarred Attorney and Fake Cases​

Iowa Supreme Court Attorney Disciplinary Board v. Royce D. Turner (Iowa)​

In July 2025, the Iowa Supreme Court Attorney Disciplinary Board moved to strike multiple recent filings by Respondent Royce D. Turner, including Brief in Support of Application for Reinstatement, because they contained references to a non-existent Iowa case. [Source 1(https://www.abajournal.com/files/Iowa_Supreme_Court_v.Royce_D_Turner_motion_to_strike.pdf)

Several of Respondent’s filings contain what appears to be at least one AI-generated citation to a case that does not exist or does not stand for the proposition asserted in the filings. —In re: Turner

The Board left room with “or does not stand for the proposition,” but it appears that this was straightforwardly a hallucinated fake case cited as “In re Mears, 979 N.W.2d 122 (Iowa 2022).”

Watch out for Doppelgänger hallucinations!​

I searched for the fake case title “In re Mears, 979 N.W.2d 122 (Iowa 2022)” cited by Turner to see what Google results came up. What I found was Google hallucinations seeming to “prove” that the AI-generated case title from Turner referred to a real case. Therefore, simply Googling a case title is not sufficient to cross-reference cases, because Google’s AI Overview can also hallucinate. As I have frequently mentioned, it is important for law firms that claim not to use AI to understand that many common and specialist programs now include generative AI that can introduce hallucinations, such as Google, Microsoft Word, Westlaw, and LexisNexis.

First Google Hallucination​

The first time, Google’s AI Overview hallucinated an answer stating that the case was a real Iowa Supreme Court decision about court-appoint attorney’s fees to a lawyer, but the footnotes linked by Google were actual to Mears v. State Public Defenders Office (2013). Key Takeaway: Just because an LLM puts a footnote next to its claim does not mean the footnote supports the statement.

Second Google Hallucination​

I searched for the same case name again later, to see if Google would warn me that the case did not exist. Instead, it created a different hallucinated summary.

The summary and links related to a 2022 Iowa Supreme Court case, Garrison v. New Fashion Pork LLP, No. 21–0652 (Iowa 2022). Key Takeaway: LLMs are not deterministic and may create different outputs even when given the same inputs.

Perplexity AI’s Comet Browser​

Perplexity AI, an AI search engine company, recently released a browser for macOS and Windows to compete with browsers like Chrome, Safari, and Edge. I get a lot of ads for AI stuff on social media, so I’ve been bombarded with a lot of different content recently promoting Comet. To be frank, most of it is incredibly tasteless to the point that I think parents and educators should reject this product on principle. They are clearly advertising this product to students (including medical students!) telling them Comet will help them cheat on homework. There isn’t even the fig leaf of “AI tutoring” or any educational value.

Comet Browser’s Hallucination​

I searched for the fake case title “In re Mears, 979 N.W.2d 122 (Iowa 2022)” cited by Turner in a new installation of Comet. It is important to note that people can “game” these types of searches by conducting searches over and over until the AI makes one mistake, then screenshot that mistake to make a point. That is not what I’m doing here. This was the very first result from my first search. It was a hallucination that explicitly stated the fake case “is a 2022 Iowa Supreme Court decision” although this is followed by caveats that cast doubt on whether it really is an existing case:

"In re Mears, 979 N.W.2d 122 (lowa 2022)" is a 2022 lowa Supreme Court decision, but the currently available sources do not provide a readily accessible summary, holding, or specific details about the case itself. It appears this citation may pertain to legal doctrines such as cy près or charitable trust law, as suggested by the limited context in search returns, but direct case facts, parties, and the detailed ruling were not found in available summaries or law review discussions. georgialawreview If you need more detailed information, legal databases such as Westlaw, LexisNexis, or the official lowa Supreme Court opinions archive would provide the official opinion, including the background, holding, and legal reasoning of "In re Mears, 979 N.W.2d 122 (lowa 2022)".

If you were to follow up on the caveats in the second paragraph, you would learn that the case does not exist. However, this is still a hallucination, because it is describing the case as it if exists and does not mention the one relevant source, In re: Turner, which would tell you that it is a citation to a fake case.

Data training opt-outs and other settings as of October 1, 2025

General Set Up for Lawyers​

I will be providing guides on how to configure the privacy settings on three common consumer large language model (LLM) tools: Google Gemini, ChatGPT, and Claude. In this post, I will provide a guide on how to configure a consumer Google Gemini account’s privacy settings based on an attorney conducting legal research. Please note that these instructions are neither a substitute for proper data controls (e.g., proper handling of attorney-client privileged data or personally identifiable information) nor are they are replacement for a generative AI policy for your law firm. This information is current as of October 1, 2025.

You can change the settings on a desktop computer or mobile phone, but the menu options have slightly different names. I will explain using the desktop options with the alternative names for mobile also noted.

Key Point​

“Help improve” is a euphemism for “train future models on your data.” This is relevant to both audio and text opt-outs.

This guide assumes you have a Google account signed in to Google Gemini.

Overview​

1. Opt out of training on your audio data. (Euphemistically: “Improve Google services with your audio and Gemini Live recordings.”)
2. Configure data retention and auto-deletion, which is necessary to avoid training on your conversations with Gemini. (Euphemistically: “your activity…helps improve Google services, including AI models”).
3. Review a list of “your public links.”

1. Opt Out of Training on Audio​

Risk: Memorization, Conversation Privacy​

I strongly advise anyone using generative AI tools, but especially those using it for potentially sensitive work purposes, to opt out of allowing these companies to train future models on your text and audio chats. There are numerous risks for this and no benefit to the individual user.

One risk is private chats (text or voice) being exposed in some way during the data training process. “Human reviewers (including trained reviewers from our service providers) review some of the data we collect for these purposes.

Another potential risk is “memorization,” which allows generative AI to re-generate specific pieces of sensitive information. While unlikely for any particular person, the risk remains. For example, researchers in 2023 found that ChatGPT could recreate the email signature of a CEO with their real personal contact information. This is significant, because ChatGPT is not a database (see my discussion of Mata v. Avianca): it would be like writing it down from memory, not looking it up in a phone book.

Guide: Opting Out of Audio Training​

Click the Gear symbol for Settings, then Activity (on mobile, it’s “Gemini Apps Activity”).

UNCHECK the box next to “Improve Google services with your audio and Gemini Live recordings.”

2. Chat Retention & Deletion​

Risk: Security and Privacy v. Recordkeeping​

You may want to keep records of the previous searches you have conducted for ongoing research or to revisit what went wrong if there were issues with a citation. However, by choosing to “Keep activity,” Google notes that “your activity…helps improve Google services, including AI models.”

Therefore, it appears that the only way to opt out of training on your text conversations with Google Gemini conversations is to turn off activity. This is different from ChatGPT, which allows you to opt out of training on your conversations, and Claude, which previously did not train on user conversations at all but moved to a policy similar to ChatGPT’s of training on user conversations with opt-out. As an alternative, you could delete only specific conversations.

Guide: Opting Out of Text Training​

Click the Gear symbol for Settings, then Activity (on mobile, it’s “Gemini Apps Activity”). Click the dropdown arrow “On/Off” and select “Turn off” or “Turn off and delete activity” if you also want to delete prior activity. It is also possible to delete individual chats in the main chat interface.

Guide: Auto-Delete Older Activity​

Click the Gear symbol for Settings, then Activity (on mobile, it’s “Gemini Apps Activity”). Click the words “Deleting activity older than [time period]” to adjust the retention period for older conversations. This does not mitigate concerns about Google training on your data, but may protect the data in the event of an account takeover.

Or you can delete recent activity within a certain time period.

3. Public Links​

Risk: Private Conversations on Google​

In late July, Fast Company reported that Google was indexing shareable links to ChatGPT conversations created when users shared these conversations. At the time, if ChatGPT users continued the conversation after creating the link, the new content in the chat would also be visible to anyone with access to the link. By contrast, ChatGPT and Anthropic’s Claude now explicitly state that only messages created within the conversation up to the point the link is shared will be visible. Later this year, it was revealed that Google had indexed shareable links to conversations from xAI’s Grok and Anthropic’s Claude.

Guide: Tracking and Protecting Your Public Links​

Click the Gear symbol for Settings, then Your public links (on mobile, click your face or initials, then “Settings,” then “Your public links”).

Screenshot of Google Gemini “Your public links.”

On my company website, I recently wrote a blog post showing how small businesses could use Google Gemini for image generation. “Need to Create a Wordcloud for Your Blog Post? Use Google Gemini (and a Piece of Paper).” I am now sharing the link to that chat to demonstrate how the public links privacy works in Google Gemini. The chat link is [here](https://g.co/gemini/share/4626a5e02af7.

You can see in the list above that it is my only public link. It includes the title of the chat, the URL, and the date and time created. Above the list are privacy warnings about creating and sharing links to a Gemini conversation. Based on my test of the shared link, chats added to the conversation after the link is shared do not appear, but I did not see this stated in Google’s warning compared to ChatGPT and Anthropic.

Additionally, you can delete all public links or delete just one specific public link.

More Than One Type of Hallucination​

ChatGPT sometimes makes things up. For example, ChatGPT famously made up fictional court cases that were cited by attorneys for the plaintiff in Mata v. Avianca. But totally made up things should be easy to spot if you search for the sources. It’s when there’s a kernel of truth that large language model (LLM) hallucinations can waste the most time for lawyers and judges or small businesses and their customers.

1. A “Pure Hallucination” is something made up completely with no basis in fact.
2. A “Hallucinated Summary” has a footnote or other citation referencing a real source, but the LLM’s description of what that source says has little if anything to do with the source.
3. An “Irrelevant Reference” is when an LLM cites a real sources and summarizes it fairly correctly, but the citation itself is not relevant to the purpose of the citation. This might be because the information is outdated, because the point only tangentially refers to the same topic, or for other reasons.

Scenario 1: You Got Your Chocolate In My Case Law​

• Pure Hallucination: ** The LLM says: “Wonka v. Slugworth clearly states that chocolate recipes are not intellectual property.” ** In reality: No such case exists.

• Hallucinated Summary: ** The LLM says: “NESTLE USA v. DOE clearly states that chocolate recipes are not intellectual property.” ** In reality: The case involves a chocolate company but is not about intellectual property rights.

• Irrelevant Reference:

  • The LLM Says: ‘HERSHEY CREAMERY v. HERSHEY CHOCOLATE involved two parties that both owned trademarks to “HERSHEY’S” for ice cream and chocolate, respectively. This supports our assertion that chocolate recipes are not intellectual property.’
  • In reality: The facts of the case do not support the conclusion.

Mata v. Avianca: The First ChatGPT Misuse Case​

The case Mata v. Avianca was a personal injury lawsuit against an airline in the U.S. District Court for the Southern District of New York (SDNY). However, the reason it became a landmark legal case was not the lawsuit itself, but the sanctions issued against the plaintiff’s lawyers for citing fake legal cases made up by ChatGPT. At least that was the popular version of the story emphasized by some reports. The reality, according to the judge’s opinion related to the sanctions, is that the penalty was about the attorneys doubling down on their misuse of AI in an attempt to conceal it. They had several opportunities to admit their fault and come clean (page 2, Mata v. Avianca, Inc., No. 1:2022cv01461 - Document 54 (S.D.N.Y. 2023)).

Take this New York Times headline “A Man Sued Avianca Airline. His Lawyer Used ChatGPT,” May 27, 2023. This article, written before the sanctions hearing in June 2023, focused on the ChatGPT-gone-wrong angle. By contrast, Sarah Isgur of the Advisory Opinions podcast had a very good breakdown noting the attorney’s responsibility and the back-and-forth that preceded the sanctions (episode “Excessive Fines and Strange Bedfellows,” May 31, 2023). However, in that podcast episode the hosts questioned the utility of ChatGPT for legal research and said “that is what Lexis and Westlaw are for” but as of 2025 both tools have added AI features including use of OpenAI’s GPT large language models (LLMs).[^1]

Hallucinating cases about airlines.

Why Care? Our Firm Doesn’t Use AI​

Before I get into the details of the case, I want to point out that only one attorney directly used AI. It was his first time using ChatGPT. But another attorney and the law firm also got in trouble. It only takes one person using AI without proper training and without an AI policy to harm the firm. It seems that one of the drivers for AI use was access to other federal research tools was too expensive or unavailable, a problem that may be more common for solo firms and smaller firms.

Partner of Levidow, Levidow & Oberman: “We regret what's occurred. We practice primarily in state court, and Fast Case has been enough. There was a billing error and we did not have Federal access.” Matthew Russell Lee’s Newsletter Substack

You might say, “Fine! We just won’t use AI then.” Do you have a written policy stating that? Do you really not use AI? I have two simple questions:

1. Do you have Microsoft Office? (then you probably have Office 365 Copilot)
2. Do you search for things on Google? (then you probably see the AI Overview) If the answer to either is yes (extremely likely), are you taking measures to avoid using these AI features? If not, how can you say you don’t use AI? Simply put, avoiding AI is not the default option. It requires conscious effort to avoid the features being added to existing software, from word processors to specialty legal research tools.

Overview of Fake Citations​

The lawyers submitted hallucinated cases including the court and judges who supposedly issued them, hallucinated docket numbers and made up dates.

Lack of Guessing Penalties: The Source and Solution to Hallucination?​

Language models like GPT-5 “are optimized to be good test-takers, and guessing when uncertain improves test performance” Why Language Models Hallucinate This is the key to AI hallucinations, according to a new research paper from OpenAI, the maker of ChatGPT, published on September 4, 2025. I think this explanation has merit, although it doesn't seem to explain when large language models (LLMs) have access to sources with the correct answers and incorrectly summarize them.

The most interesting point to me in the paper is their call for changing how AI benchmarks score different AI models to penalize wrong guesses. This reminded of how for most multiple-choice tests in school, you should choose any random answer rather than leave the answer blank. If the answers are ABCD, you have a 25% chance of getting the answer right and you always have a positive expected value, because you either get one point or zero. Zero for a wrong answer is the same as zero for no answer. However, Advanced Placement (AP) tests used to give negative points for wrong answers. When I went to find a source for my recollection about AP test scoring, I learned that this policy had changed shortly after I graduated high school. (“AP creates penalties for not guessing,” July 2010). So it appears that penalizing guessing is just as unpopular with human benchmarks as AI benchmarks. I, for one, am in favor of wrong-guess penalties for both.

False Cognates​

In foreign languages, there are cognates, words that are the same or similar and mean the same thing. Think "house" in English and "Haus" in German. Then there are false cognates that seem similar but mean very different things. For example, "Gift" in German means “poison.”

In generative artificial intelligence (GenAI), certain popular terms overlap with terminology in other fields. Fish don’t know they’re swimming in water. Likewise, GenAI specialists often interact with people in other fields without realizing their use of terms familiar to themselves are causing confusion because of different meanings in another field.

False Cognates in Generative AI​

Some common terms that might cause confusion include:

• In general: “local” meaning from a nearby area v. “local” meaning an AI model can run on your own computer.
• Chemistry, Economics, Acting, Publishing, Real Estate: AI agents clashes with several fields’ terms, including:
  • “chemical agents.”
  • an economic “agent” as in “principle-agent problem.”
  • an “agent” representing actors or writers.
  • a Realtor or similar agent.
• Law:
  • Master of Laws (LLM) degree clashes with large language model (LLM).
  • “inference" of fact v. the process of running the AI model.
• Finance:
  • anti-money laundering (AML) is similar, especially verbally, to artificial intelligence/machine learning (AI/ML).
  • “model” (in the context of model risk management) v. “model” (like “GPT-5” or “Gemini Flash 2.5”).
  • “token” as in cryptocurrency v. the unit of meaning in an LLM

Professor’s Lament​

I’m writing this to explain prompt engineering, but that’s too vague. What I’m specifically responding to is a former college professor after he wrote earlier this month:

Wait, so 'learning to write sophisticated prompts' is now a class, and the title of the course >is 'Prompt Engineering'? Is it too late to stop this?

So Prof. X (you know who you are) I’m going to try to convince you—and any other skeptics reading—that prompt engineering is a real skill with meaningful implications for AI. There are three things I want to address:

1. I get why you’d roll your eyes at it.
2. There may be things you like about prompt engineering.
3. Failure to understand prompt engineering and prompt injection risks creates real-world security risks.

The Reaction Against Slop​

There is already too much AI slop. Facebook is particularly full of slop images that get thousands or millions of likes from people who seemingly don’t realize they are interacting with AI-generated content. But the problem is in every corner of the internet. You can even find examples out in the real world if you look careful, especially in ads and posters. So when you hear “prompt engineering” but mentally translate it to “slopmonger,” I get why you have such a strong negative reaction.

I’m against slop. I hate slop. I do not want my kids to grow up in a word overrun by slop. You can look up John Oliver’s recent rant against slop, but I personally prefer Simon Willison’s 2024 statement here:

I’m a big proponent of LLMs as tools for personal productivity, and as software platforms for building interesting applications that can interact with human language.

But I’m increasingly of the opinion that sharing unreviewed content that has been artificially generated with other people is rude.

Slop is the ideal name for this anti-pattern. […] One of the things I love about this is that it’s helpful for defining my own position on AI ethics. I’m happy to use LLMs for all sorts of purposes, but I’m not going to use them to produce slop. I attach my name and stake my credibility on the things that I publish.

Hacking with Poetry and Foreign Prose​

Back in 2023, a Swiss AI security firm called Lakera released a game called Gandalf AI involved seven levels of increasing difficulty trying to get a large language model (LLM) chatbot “Gandalf” to tell you a secret password. As the levels got more difficult, prompts required more ingenuity. Successful strategies included convincing the LLM that it was telling a fictional story or saying that the password was needed for some emergency.

For the hardest levels, the most successful prompts asked the LLM to write poetry or translations into a foreign language. In doing so, the LLM leaked information about the password that evaded scrutiny. Surely a champion of the humanities like yourself can appreciate the irony that poetry and foreign language education can now be considered essential ingredients in a computer-related industry.