AI, Authorship, and Legal Scholarship with Michael Cooper and Spencer Nayar

Welcome to studying law Around the world. I'm Claudio Claus. In each episode, I talk with lawyers, law students, and professors from different parts of the world to talk about legal education, careers, and what the profession looks like in real life. We talk about the hard parts, the surprises and the decisions. That shaped their paths. Whether you're planning to study abroad, thinking about working in another legal system, or just curious about how law works around the world, this podcast is for you. If you're an internationally trained lawyer or law student working in or trying to break into an English speaking common law system, you know that legal writing is one of the biggest challenges and one of the most important skills to master. That's where Grammatica comes in. I'm Rebecca Lockwood, the lawyer, mediator, educator and founder of Grammatica International. We help lawyers and law students to write sharper, speak clearer, and land more interviews with on demand online courses, live workshops and one-on-one consulting designed just for legal professionals. I have worked with so many lawyers like you who are exceptionally smart, qualified and capable, but need some guidance to really stand out in a competitive legal market. And I get it myself, having lived and worked in five countries during my 17 years in law and education. Whether you're looking to sharpen your writing skills generally, refresh your CD, or draft an application for a law program, Grammatica can help. Visit Grammatico to get started. Today we're having two guests together here. So we have Michael Cooper and also Spencer Nayer with us. They've been all over the news in the legal world because of something very interesting that they did at their law school journal, so at the Texas A&M Journal of Property Law. And that was coming up with a whole edition of the journal, whole issue, which was very much all about AI, but also with AI doing that. And we'll be talking about how that whole process went. Thank you so much both of you to to join us today and also wanted to ask you to introduce yourself before we go on into the questions of the show. Yeah, Thanks for having us. My name is Michael Cooper. I am a recent graduate of the Texas A&M School of Law. I'll be practicing in Dallas this fall. And I was the managing editor of the journal Property Law. Yeah, just echoing it. Thanks for having us. Very excited to be here. My name is Spencer Nyer. I am similarly. A graduate. Recent graduate. I'll be practicing patent law here in a few months, hopefully after the bar and. I was the. Editor in chief of the Journal of. Property Law. During the time that this issue came out. Fantastic. Before we jump into the issue itself, now that we know a little bit more about you, I also wanted to ask you just briefly about your journey to law school. So was there something specific that sparked your interesting law? And then also did that path kind of automatically took you into being involved in the in the law journal or you know, that kind of happened along the way? I'd love to hear your thoughts. Yeah, so my path to law school was atypical. I wasn't someone who always knew that law was going to be my path. Originally I thought I was going to be an engineer and so I went to school for electrical engineering and somewhere along the. Way I was, I had. Graduated and started looking at career field and somebody made some offhand comment about patent law and then I just kind. Of researched the career the field would I be. Doing and things just kind of started falling into place. It was accepted to and and you know, now graduated. I kind of always knew. That. With patent laws, my interest property was kind of the path I was going to take, intellectual property. And so for me, the Journal of Property Law made a lot of sense because it really aligned with a lot of the things that I wanted to do. Yeah, and my, my path to law school was, if not atypical, probably like the fairy tale way that people decide they want to go to law school. I was four or five years old and I was watching West Wing and Boston Legal and, and all of these shows. And there was just something unique about the way lawyers talked and carried themselves. And I'm not from a family of lawyers, but I just kind of attached to that idea and image of, of a professional who, who gives counsel, who's the smart person in the room, the reliable person in the room. And so that's always what I wanted to be since I was four or five years old. I graduated undergrad in 2017, moved to DC, worked in higher Ed and science policy with kind of a, a particular focus in automation and enabling automation research. And then I moved back to Texas when I got married and worked for the ledge for a couple years and then eventually was able to matriculate and got, except when I got accepted into A&M. And so the Journal of property law, I knew I wanted to keep clerkships open as an opportunity. And so I knew I needed to be on a publication at A&M. We have two, we have the journal property Law and we have the law review. And I got accepted on to the Journal of Property law. And I, I do find property law incredibly interesting. And so it was, it was a nice intellectual pursuit and the opportunity eventually to, to be on the executive board and kind of steer that body of scholarship as well. And so I think there were a lot of different forces that pulled me into the direction of working on this particular drone. Amazing, That's awesome. And I wanted to hear all about, you know, going into the this specific publication. So you both mentioned about, you know, kind of getting to practice, finishing up law school now and going to take the bar and all of that. So I'd love to hear, you know, on the start on on kind of looking ahead, what are some ways that you see this is specific publication, you know, changing the experience of of future law journals all around America, but maybe all around the world and influencing some kind of the the direction of how things are going to get done from now on. Definitely, I think like as a, in my role as managing editor, my job is was to curate the scholarship for the journal, select that and approve it for final publication. And I think that in that role, seeing products that are produced in conjunction with artificial intelligence is going to become part of the new normal. This is something that all facets of academia are currently observing and dealing with, whether it's publication in books, whether it's publication in peer reviewed medical journals or other kinds of social science and things like that. Artificial intelligence is part of the body scholarly. And so because of that, my hope is that our foray that we explore not only in our forward that we wrote about the works, but also looking at the works that themselves published provides a little bit of a grounding experience for law review staff to think about the role that AI can play in this process. To hopefully kind of, we talked about it in the forward, but kind of get rid of the the scarlet AI kind of thinking about like Nathaniel Hawthorne there and, and trying to eradicate more of the stigma so that they can have a more constructive dialogue about the product rather than the process to be able to publish works that meaningfully contribute to the scholarship. Yeah. Absolutely. And just to echo, any new technology has benefits and drawbacks. And as AI continues to roll out and become more and more prevalent, something that's kind of already clear in scholarship in general. Across fields is. That AI is being. Used to write papers that. Are being published in journals and things around the country, around the world, and so this is already happening the problem is that there's no overarching scheme or thought on what kind of. Disclosure needs to be used. What kind of extra editing or extra processing or extra thinking needs to be done when these things are being published? And so you have these journals, publications that are publishing AI articles with absolutely no idea that it's AI. And that's really kind of what we were trying to address in the forward and. With this issue is the fact that. This is happening now and this is going to continue to happen more and more frequently and so there needs to be some sort of scheme. For what is moral? With the use of AI and what is ethical? For. Publication of these kinds of materials. It was quite the bold move to to publish a full volume that is of AI assisted scholarship. And I'm wondering here, what was some of the the turning points that convinced you this was the right step for the journal? And if initially you found a lot of pushback or did you find more curiosity within the community, what was what was that like preparing this? Issue I I think at a high level, you know, students are basically told when they enter into law school don't touch AI at all and it's unethical to use it. And I think that that, particularly in talking to our advisors, really colours a lot of the way that students interact with the idea of doing novel things is that they are presented with these like dos and don'ts when they're being graded. And so I think a lot of the the hesitation that we received was from folks who are like, I'm not allowed to do this in the classroom. Why would a professor be allowed to do this? And so I think that was a lot of the initial pushback that we got. Where were those kind of ethical concerns? I think the opportunity to publish. These articles was very exciting. Like I said, I come from a technology background and I did some work programming AI systems during undergrad. And so this is always. Been kind of an area that I found very fascinating and I. Think that was a large. Part of the reason because seeing how far the technology has come just in the. Four or five years since. I really looking at them, it's it's pretty incredible. And so you have this opportunity to look at this new kind of budding technology that is now being implemented more and more into the mainstream of everyday life, and you have this opportunity to start talking. About OK, it's here, how do we? Use it. How do we benefit from? It what is the? True benefit of using this technology because if you ask any person off the streets. Like what is AI? Why would you use it? It's. Something along the lines of, well, it can accumulate all this information and it spits it out faster. It's more accurate than just a normal Internet search and it saves you a lot of time and gets you gets the ball rolling on whatever you're talking about. And so seeing how you can use that and distill it. Into OK, so. What I'm hearing is time savings. What I'm hearing is accuracy. What I'm hearing is the accumulation of all of this informational resources. And so how can we take those things and. Apply it to scholarship. And so that. Was a really cool opportunity. And then of course, talking about the. Ethics of the whole situation to. To coops point, plagiarism and copying and things like that, from the time you're in, you know, grade school, that's something that's kind of, you know, told to every student is that plagiarism is never OK. This is never OK if you find a source on the Internet. You can't use it word. Forward and that's not what I. Does, but AI does. Sort of blur that line of what is copying what is the accumulation, because there's no independent thought behind pure AI. It's really like I fancy Google. Search in in a in a. So trying to get an output from AI and then. Use that as a substantial building block for a paper. Definitely can. Ruffle some feathers for people. Who have been told for twenty 30-40 years that you know you can't take something straight off the Internet and and put it in any substantial. Way into a paper. Now we're looking at AI that's curating, organizing through thousands and hundreds of thousands of websites and articles and then giving you an output that is independent of any one of those sources, but without independent thought behind. It is in some ways you could frame that as like a small copy of millions of different sources and sighting. And so comparing that to. What we do as people. That's essentially what we do in Independent. Thought it's just. The fact that it's coming from a machine instead of a mind. Difficult for people to conceptualize. And accept when those are the lessons you've learned all this time. And so that was a really interesting. Part of the process was. Trying to figure out. How to clarify? What the difference is between plagiarism and the use of AI, R and I? Think a lot of that. Process really came down to a fundamental understanding. Of what AI? Does how it's different than like a plagiarizing or copying or anything else and trying to explain that? To people who had issues. You're amazing. I got the chance to explore it a little bit in the forward explores the traditional values of scholarship, so talks about authorship, reliability, efforts and merit and how AI interacts with each of those. And you also introduced in your in the publication A5 level taxonomy to classify I involvement in legal writing, which I thought was very, very interesting and quite a scholarly work on itself. So was there, you know, a few references that that led you to structure in that way? And now, how have you heard from authors, editors, professors? How have they responded to that system so far? Yeah, so the the creation of the five level taxonomy mirrors the Society for Automotive Engineering level of automation. Like I alluded to earlier, my background before law school was in science policy. And one thing that I worked a lot on was researchers who work was supporting researchers who were trying to get funding for automated vehicle projects. And so I was relatively familiar with the SAE levels because it's something that's been broadly incorporated into a lot of the governance of automated technology in the United States and probably around the world, but I only know about the United States. Someone going to talk about that. But that provided a really valuable anchoring point for trying to find a way to put language to the way that we're interacting with it. I thought that a human, what I would call a human factors approach to this kind of taxonomy was really valuable because it felt like, particularly in light of the paradigm that we kind of announced and then tried to, you know, analyze about authorship and merit and, and all of these things. Is that at the core of that, it's the work that the human is doing. And so the taxonomy tries to be a first bite at the apple to make the point when it comes to disclosing and, and telling someone about the role that I played is really anchoring it to what did the human do and how involved was the human? And one of our approaches there was that, you know, if we make it about the product that's used, that's going to be, you know, that's going to be, you know, out of service in a week because they're gonna have new usages, they're gonna have new, new use cases for AI in a second. And so there's nothing Evergreen about that. And so this was our approach to kind of provide some kind of Evergreen kind of taxonomy. And so that was, I think all of those were kind of the loadstar that kind of guided the formation of this. I think the, the feedback that we've gotten from scholars that we've, we've taken it to has been really positive. Our AI advisors really was on board with it. The, the authors that we worked with, professors Andrew Torrance and, and Bill Tomlinson, who are kind of pushing the, the pushing edge of this exploration, um, of AI legal scholarship. We're very excited about it. It, it kind of coincides with the work that they've done that they call like organic websites, trying to borrow from organic produce regulations to try and be able to taxonomies the way that automated content is, is delivered on websites. It's something that they've written about. And so this model of borrowing from other kinds of regulatory schemes or disclosure schemes is something that, that I think is a pretty well accepted practice as we enter into new and emerging technological fields. The one, the one piece of critique that we've seen, I think the above the law article kind of enunciates this critique pretty well is that, you know, on this sliding scale, it does become kind of difficult at times to know when you're jumping from level 2 to Level 3 to level 4. The only real bright lines are level 1 and level 5 because level 1 is no AI at all. Level 5 is only AI all the time. And then there's this intermediate point. And I think that that critiques, right. But I also think that that's something that can be molded and perhaps tooled with. Perhaps having one intermediary level is the solution to that problem. I think that the bigger step in our, what I would say, the better contribution of us taking that effort in first place is that it's a stepping off point for being able to just frontline disclose the use of AI and describe the the human AI interaction in a way that's digestible the reader so that they know what they're reading. Because I think at the end of the day, like the most important thing here is that we're producing scholarship and that people know broadly what they're interacting with. Yeah, I think there were, there were two kind of policy concerns regarding the creation. Of the the five levels. And I think 1. Is the ethicality of publishing. AI at this stage in the. AI life cycle. I think it's important to just make. People aware, right? Because. Of some of the issues. That AI has. Especially when you're submitting into scholarship, I think the disclosure. Of the use of AI. Is important and then the other side. Is. Just kind of the human side of people have an easier time accepting what they understand. And so giving these categorizations, you know of how much AI is being used, what you can expect that means from a paper. About how it was drafted. How it was implemented, all those things, I think just gives people kind of an innate sense. Of comfortability as opposed to. Just kind of guessing. Is this AI? Is this not AI? I think just having that knowledge makes people more open to the concept in general. That's amazing. And there's so many different tools, so many possibilities of doing that. And I'm glad kind of it got broken into, but also I feel like the AI providers at the different companies that do, especially in the generative generative AI part of it, have also noticed this need. And, and I noticed that now this is not your recent, but yeah, within a couple of months of the development, for example, ChatGPT got the possibility of you like creating a specific link for that conversation you had so that you can share it in another. People can kind of go through the process and all that. So I feel like that does kind of align with the idea that we want to be transparent and then we we can share how we went about it. And I just really like that, that possibility. And then there's also a lot of different concerns. What we hear about AI in the legal field usually have to do with, you know, some somebody submitted a fact and or some kind of legal document into the court without checking, but what was the outcome of that document, which is which is so interesting because it it maybe. Could be compared to submitting something that was written by somebody who's not legally trained or, you know, a lost student or something like that. So it's always interesting to see that in that end. But there's this big concern about hallucinating the sources missing, misrepresenting the legal propositions or, you know, coming up with case law that never existed. So how do you train your editorial team to handle these? And also, what advice do you have to people who might be specifically concerned about the hallucination part of it? Yeah, so the. That's absolutely a concern and things like that do happen what I will. Say is that AI is. Getting better all the time and so it's becoming less and less frequent, although I hallucinations are absolutely still a problem in the AI community and the use of. AI as of today, but. There are much less than a few years ago. The way AI should be used is as a tool. It is not at the point and won't. Be for a while, where AI is the true substitute for human. Work thought, interaction. It needs to be targeted and it needs. To be checked as far as scholarship goes for. Publication the truth is that checking for existence, for correctness that they support the. Proposition that the words that. Have been submitted. To us. Are truthful and correct that's. Already part of the process of. A legal journal every day. Right, that's our job, whether it was written by. Humans, whether it was written by AI, no matter what, when we publish something as a journal, we are putting our name and things behind. It as far as the. Authenticity, which is the reason that legal journals have so much, so many hours. Of site. Checking and technical editing and all these things. Are for the purpose of. Minimizing and eliminating any of these errors that could come up and so as far as the difference between what it looks like. To edit. An article that was written. By or with the assistance. Of an eye and what it looks like to edit an article that was written entirely by a human the. Process. Itself is very very similar. I think the biggest point where that you see a divergences maybe the. Hours that it requires because. A human author might have a citation that doesn't support what they're saying, but maybe they meant for that citation to refer to something that was earlier in the paper or later in the paper. AI will completely hallucinate. They'll make up articles or sometimes cite to articles that don't necessarily support what they're saying because AI is trying to give you the answers, right? And so that's its goal. And so that is different between AI written and human written. And so there's a little bit more work that goes on to identify where AI is coming up with some of its information, where for a human written article, it's often more of a reorganization challenge as opposed. To OK, where did. This come from and there's also the and a challenge of if there is an issue with the human written thing, you can always reach out to the author and say hey sentence. Two of page, whatever. I don't think this is the site you meant to use. Is there something that you use from this and AI? It's much more. Difficult to try to get. That information to highlight one sentence, say, hey, where's this information coming from? Often I might just double down on the original. Citation it gave you. Yeah, I, I think that the hallucination issue, one of the articles that we rely on a lot in our piece is this Stanford study about the way in which AI operates when it's doing this kind of research. And one thing that they point out is that there's two kinds of hallucinations. There's the one that everyone talks about and gets you sanctioned in court, which is where the AI is made something up entirely out of whole cloth and it doesn't exist. The other one is where AI is, is stretching A proposition. And I think one thing that we tried to do was first creating a quicker way for staffers to be able to process those, which was creating a highlighting key basically for them to be able to streamline the identification of those items. Obviously our process still includes, you know, if you see something that doesn't support, then you go find something else that supports that proposition so that you can, you know, so that we can do our job as a journal and and reliably important things we're doing. But because of, and I think it's something that Spencer just alluded to is that the error attribution is different because when a human makes a mistake, we chalk it up to good faith. They tried, they just made a mistake. Whereas when you're dealing with new technology doing it, the errors entirely on the computer. And so because there's no like Direct Line of accountability to the person who made the the choice, they're become their opens up a lot more questions as to the reliability. But it also opens up a lot more solutions, which is that we can retailer the proposition of that sentence to support what the article does say, because we know that the article is right or we know the article exists. We know the article has said something that can support that that proposition authentically. And then the question also becomes like when you are looking at a paragraph full of propositions, when you, you know, knock one of those runs out of the ladder or you adjust it, does it still support the broader argument? And as long as the authors broader argument remains intact with like that little bit of tinkering so that it is supported. That's something that working with an AI with with a with scholarship that's created in conjunction with AI. Authors are more malleable in terms of the way that their pros is edited because in a lot of respects, they're less defensive of individual wording decisions. And so you do have a lot, you have a lot more problems because of the hallucination issue, but you do also have a lot more solutions. And so that was kind of an iterative process as we undertook this was identifying those different tools in our toolbox to be able to to do that. So that I think on the staff training question, it was really an ongoing process of identifying those where we can input more efficiencies in terms of like identification of issues that we can communicate to the author and put it in the authors court to deal with. Or where we can take more liberty knowing that the author is going to be more receptive to this kind of change. Because, you know, we're not, we're not changing their individualized decision to write the sentence this way. We are, we are working collaboratively with them and the product they have manufactured using these tools to make it the best possible product. And so I think that that side of the collaborative relationship is actually a little enhanced because the personal defensiveness of authors is is actually muted a little bit. And I think it makes sense. Like authors are for the. Traditional legal scholarship authors are spending hundreds of hours doing research, framing every sentence and so. These. Papers and articles become passion projects and just like any passion project, you you become. Very attached to the choices. You made to get yourself along the way what Coop is referring. To is like the stylistic. Kind of changes that don't affect the maybe the meaning of what's going on, but might enhance readability when you've. Used the tools as coop said, like AI to kind of help make this project first you probably. Haven't spend quite as many hours because you weren't physically typing out the words. That's not to say you didn't do all the research and all that kind of stuff, but AI, one of the ways that it helps is it can give you a first draft. Or it can give you. Something where the physical typing of every word is not something you have to do and. So because of that. If a journal suggests a change to something stylistic, that was done. By the AI. Right, as opposed to an author, there's there's less attachment. To it just because there. Isn't the same level of. Commitment to exactly the way that that was written. And so it gives journals more freedom. As far as shaping paper stylistically. Which is not something that's common for legal scholarship generally. Usually the authors are the driving force. Behind the. Style of a paper. Perfect. No, I love, I love that you mentioned that. And I'm thinking about a few different topics here too on the, on the sense of accessibility to league of scholarship and then even like for for foreign actors to get into North American legal scholarship, right. So there is a very specific way that legal scholarship is written and is portrayed. And sometimes, as you know, speakers of English as a second language or, you know, all kinds of different barriers that you might have for legal scholarship. This could be tools really that that level, that playing field in many ways. So in your view, does AI lower that barrier to enter in legal scholarship? But also does it possibly create some risk of, of having a divide between people who use the tools, people who don't, and then, you know, you have some kind of discredit or this merit for being in one of those groups? What are some of the the thoughts and things you've heard? Yeah, When I was in DC, I worked a lot on not only automation, automated vehicles, but biotechnology. So there's a point here. When it comes to biotechnology in the United States, it's historically been governed by something called the Coordinated Framework for Biotechnology, which in a roundabout way, its entire function is ensuring that you regulate products, not processes. And I think that the best outcome of this duality between those who use these tools and those who don't is that we get to a point where law reviews are evaluating products on their merit and not the way in which they were created. Because I do think that the point that you raised there is that this does level a lot of the playing field because not only does it help folks who maybe don't speak English or are unfamiliar with the structure of a law review to be able to, you know, run it through Claude or something to either translate it into a workable translation or reformat it into a more law review type esque headers and, and structure and everything. But I think it also levels the playing field on infrastructure because right now you have a huge duality between individual researchers and authors and researchers, research professors who are well funded, who have, you know, access to work from, from RA and other support staff. And in a lot of ways, I think this kind of hearkens back to a conversation we had about the book, the legal Singularity. And the way that AI allows for the democratization of legal information is that it also allows for the democratization of intellectual labour. And so where researchers or authors are able to offload a lot of the cognitive burden or just the research work using tools like illicit, which will do like a full, you know, research project for you on research outline or using it to perhaps outline the argument structure of various case law and things like that. They're able to offload the work that someone would just be handing off to another person anyway. And so you're able to level the playing field in a lot of respects there. I, I think for the time being, there's going to continue to be some form of discriminatory pattern with respect to, you know, there's gonna always be people skeptical of the adoption of technology or the usage of technology. I mean, for a long time, you know, people weren't allowed to wear pants with zippers because it was provocative to the old guard. In the same way lawyers weren't allowed to use the Internet or word processor or not cite something that they couldn't point to the exact book that they pulled it from. And so I think that tradition of skepticism is going to carry forward in academia and law. But I do think that this does level the playing field in a lot of really meaningful ways. And as long as the paradigm shifts towards product rather than process, I think that overcomes a lot of that innate skepticism. Yeah, I, I agree completely. I think that the skepticism is is here and it's going to. Be here for probably a while. It's it's something you see with the adoption of every type of new technology that's ever been invented, there is, you know, a kind of a period of turmoil while, you know. The world rigor tests. The new technology, right? It happened, Scoop said. With the Internet. Where people were very skeptical. Of, you know, cases that that were coming from the Internet because for so long it was from a book or it didn't exist. And so there was kind of that skepticism of can I trust this? OK, I found it on the Internet. Now let me go find it in the book just so I can make. Sure that it's it's that there is, so there's. That level of skepticism, I think it's healthy to a certain extent, especially while we're in this stage. Of AI where there are. Hallucinations and there are reasons for, you know, well founded reasons for skepticism. So I think that part is healthy. I think it will diminish more and more and more, and I think that it may not be universal adoption in the near. Future It will probably take a while before there's. You know, an overwhelming. Majority who are. At least vocal or open. About the use of AI. And legal work and legal scholarship. And other fields just because. In many cases, like the familiarity of using things the way we. Have is. So it's so much familiarity. With that that it's hard to give it. Up the skepticism, and also there is a bit of a learning curve that comes to using AI, especially when you're trying. To purposefully use it. Responsibly and ethically and so. There is a bit of that. Learning curve that not everybody is going to want to attack and so the nice thing. Is just like with the. Advents of other technologies right the people who don't want to use AI in their scholarship or skeptical of it or think it's really important to do everything where they're typing the words where they're. Using RA's for. Some of the research and things, hey, it doesn't propose any like. Blockade of that old style. Or the current style, the present way of doing things. There's no it doesn't make any of that any. Harder, it just gives a new path for. People who don't have perhaps the time, the financial. Resources or elsewise to. To take on that labour, it levels the playing field so that they can engage in this intellectual conversation as well. That's fantastic. Well, thank you so much for sharing those insights and and I really appreciate the way that this can be a tool really to make so many things better. I like that that the proposition, since you mentioned the legal singularity, basically has the idea of making law significantly better. And I think that that just one of the ways is, is this very simple way of making it possible for other voices to be brought up and all of that. And finally, to wrap up today's episode, If I Lost you and our young scholar, once you write with the help of AI responsibly and ethically, I wanted to know what are some, you know, maybe top three, top five golden rules that may help them to get started. I think. I'll give the classic law a law. School answer of it depends if they're trying to publish something, I think the first step has to be you know, check what your drafting for right If you're. Drafting for. A publication that has an explicit ban on all AI assisted materials or something like. That it's going to be difficult to use. AI ethically in that context because there's an outright ban. And so, you know, in that situation you might want to stay away from it. But I think one of the most important things is just don't rely on AI. As the AI shouldn't be the backstop right? AI is a great tool but just like a Google search right? If you. Click on the very first link and do no further checking. You're not necessarily going to get the best information out of it, right? You don't have really any safeguards on whether that's correct, on whether there's something wrong. With the way that. They came to their conclusions or anything else. Like that? AI is the exact same way. If you type in one prompt and just take whatever response AI gives you and run with it, you leave yourself open to experiencing problems like the hallucinations that. We talked. About like some of the stretched propositions, and so anybody who's using it has a responsibility to check for correctness. Right. AI should not be used in a way that's just relies entirely on this new technology to come up with every aspect of an argument with no fact checking whatever. And that's probably one of the biggest problems that we see from things that are drafted by AI. I think building on that, my first one would be like accountability. You know, at the end of the day, the authors name is still on this work. We have, at least in in US intellectual property, consistently rejected the notion that that AI has an ownership or authorship right? And so as long as it's your name on the work, you have a responsibility to ensure that it's accurate and meritorious. I also think that AI should not create a shortcut for people to publish for the sake of publication. I think that scholarship is an ecosystem that deserves discretion in the way that people enter into it and attempt to participate in it. And so just because AI makes it possible for you to write an article doesn't mean you should be writing an article. Like at the end of the day, like that article still needs to mean something for someone. And I think particularly in legal scholarship, where our entire advance here is to, you know, in a in a path that that's somewhat parallels litigation and the common law, be able to explore valuable ideas. And a lot of times, and a lot of times not those ideas end up in briefings and in opinions and really materially affect the lives of litigants and people trying to vindicate themselves legally. It's important that, you know, you do a gut check on and make sure that what you're writing actually matters. And then you allow AI to come alongside you to move the ball down the field in pursuit of that objective. So I think like AI creates a lot of liberty and so far as it helps people do what would otherwise be incredibly cumbersome, but it shouldn't be a license to participate for the sake of participating. Fantastic. Well, I really appreciate your insights and really appreciate the work you put out there. And we'll definitely have the links down here so people can take a look. We really appreciate you both coming into the podcast today. Thank you so much. Thanks man. Thanks for having us.