In this episode of The Geek in Review, hosts Marlene Gebauer and Greg Lambert interview M.C. Sungaila, an appellate attorney and the host of The Portia Project podcast. The podcast is geared towards highlighting women in traditional and non-traditional legal careers and is set to celebrate its 100th episode during Women’s History Month in March. M.C. Sungaila initially intended to highlight women appellate judges and justices in a book, but quickly realized that a podcast would be the best medium to capture the stories of these women. The podcast now includes women leaders across the industry and beyond, providing a career touchstone for law students and showcasing where women are leading inside and outside the legal profession.
The Portia Project podcast explores a range of courts, including state, federal, and magistrate courts, as well as the process of becoming a judge, and was a finalist for the California Legal Award for Innovation in Diversity and Inclusion. M.C. talks about partnerships with organizations like Girls Inc. to amplify their work. The podcast eventually expanded beyond the judiciary to include legal tech founders, legal design innovators, and others who are making an impact in the legal world. M.C. Sungaila encourages law students to explore these new career paths.
There is a common thread among the guests in that there is no straight path to success, and everyone has unique experiences and skills that lead to their success. M.C. emphasizes the importance of recognizing that success can be different for everyone, and there are many paths to success. She plans to continue focusing on women judges, especially appellate judges, and to include more unique journeys and different approaches to legal practice in the podcast. Additionally, she hopes to branch out beyond the legal industry to bring in guests from other disciplines to provide new thoughts and ideas for women in the law.
M.C. Sungaila discusses the disproportionate share (in a good way) of women on the Supreme Court benches in Michigan and Washington and the desire to diversify the courts in those states. She also talks about the lightning round questions she asks her guests and how it helps her get to know them as people. M.C. shares her optimism for the future of women in the legal industry and the importance of being people centered. We ask MC about her motto, which she attributes to her mother’s notes to her throughout her career, such as “make this the best day ever” and “paint your canvas with your own brush.”
M.C. Sungaila’s Portia Project podcast is an excellent resource for law students and individuals interested in learning about the diverse career paths and approaches to legal practice for women in the legal industry.

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Continue Reading Breaking Barriers: The Portia Project’s MC Sungaila on the Unique Paths to Success for Women Lawyers and Judges (TGIR Ep. 192)

The promise of AI has been around for decades, but it is the last three months that has finally caused an awakening so forceful, that even the legal industry understands it needs to be ready for the upcoming Age of AI. This week’s guest has worked toward that goal of integrating AI and other technologies into the practice of law for more than forty years. Johannes (Jan) Scholtes is Chief Data Scientist for IPRO – ZyLAB, and Extraordinairy (Full) Professor Text Mining at Maastricht University in The Netherlands. He joins us this week to discuss the need for lawyers and law firms to use these tools to enhance the power of the practice of law. And he warns that if the traditional legal resources of lawyers and firms won’t step up, there are others who will step in to fill that void.
While the AI tools like GPT and other generative AI tools have finally begun to be true language tools, there is still a lot that these tools simply cannot do. Scholtes says that there is plenty of legal work to be done, and in fact perhaps more work now that the computers can do most of the heavy lifting and allow the lawyers to do the thinking and strategy.
Scholtes compares the relationship between the lawyer and the technology to be that of a pilot and co-pilot. A relationship in which the co-pilot cannot be completely trusted but can be trained to assist through the process of vertical training. This means that a law firm needs to work with the AI to have it better understand how to process legal information. Having the technology alongside the lawyers provides a stronger legal representation than just the lawyers or the technology alone. In addition to reducing risk and improving outcomes, Scholtes also projects that Lawyer + AI means higher rates and better profitability, while the clients receive better results.
It is exciting to be at the beginning of this change in the way law is practiced. It is important, however, that law firms, lawyers, and legal professionals understand how to teach and control the technology, and that there needs to be transparency in how the tools work and make decisions. His recommendation is that if all you are offered is and AI Black Box, then you should simply walk away. That lack of trust will come back to bite you.
For more insights from Jan Scholtes, visit his blog, Legal Tech Bridge.

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Continue Reading Johannes Scholtes: AI Is Finally Here. Now the Hard Work Begins for the Legal Industry (TGIR Ep. 191)

It is pretty apparent that we are in a super Hype Cycle when it comes to AI tools like ChatGPT, but for many of us in the legal profession, we’re not used to reaching this point of the cycle at the same time as the rest of the world. Because things are happening so fast, we wanted to bring in someone like Colin Lachance from Jurisage to talk about how they are integrating Generative AI tools into their products.
Greg was going down an AI rabbit hole on Twitter this week when Colin mentioned his own project he was launching. Jurisage’s tool, MyJr (pronounced “My Junior”) is part of a joint venture between Jurisage and AltaML, and is designed to change how researchers access information by allowing the AI tool to synthesis and read cases as the researchers search and analyze the information. Rather than opening up web browser tab after tab and scanning cited cases for relevant information, the idea behind MyJr is to have it quickly answer that information for you. If you need to know what the relevant arguments are from each side in Smith v. Jones, as MyJr to pass that along to you. Ask it a plain language question, get a quick and plain language answer.
Lachance is working to use the GPT 3.5 tool to pass along cases and create what he calls “guardrails” with the cases so that the prompt and the results limit themselves to the case itself. This protects the researcher from the AI “creating” the answer from all the non-relevant information it has collected in its large language model of machine learning. Lachance has additional goals for using AI within Jurisage’s data, but he’s focused tools like MyJr establishing trust with those using it for researching Canadian, and soon US caselaw.
The MyJr product works as a browser extension and identifies Canadian and US case law citations on any web page. It delivers a preview into key details about the cited case, and a link to a free full-text version, in a popup when the user hovers over the citation. Clicking through to a “more insights” dashboard reveals additional detail as well as access to the upcoming “Chat with a case” feature (Feb 20th for Canadian case, a month later for US). While the paid version of the dashboard won’t officially launch until late March, user can get unlimited pre-sale access today as well as secure a future 50% discount option for a one-time payment of $7.

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Continue Reading Colin Lachance on Jurisage’s MyJr and How He’s Looking at AI to Assist in the Synthesis and Reading of Legal Cases (TGIR Ep. 190)

This week we have Damien Riehl, VP, Litigation Workflow and Analytics Content at FastCase, and one of the drivers behind SALI (Standards Advancement for   for the Legal Industry.) Damien is definitely a “big thinker” when it comes to the benefits of creating and using standards for the legal industry. SALI is a system of tagging legal information to allow for better filtering and analysis. It works like Amazon’s product tags, where a user can search for a specific area of law, such as patent law, and then choose between various services such as advice, registration, transactional, dispute, or bankruptcy services. The tags cover everything from the substance of law to the business of law, with over 13,000 tags in the latest version. SALI is being adopted by major legal information providers such as Thomson Reuters, Lexis, Bloomberg, NetDocuments, and iManage, with each provider using the same standardized identifiers for legal work. With this standardization, it will be possible to perform the same API query across different providers and receive consistent results. Imagine the potential of being able to ask one question that is understood by all your database and external systems?
In that same vein, we expand our discussion to include how Artificial Intelligence tools like Large Language Models (i.e., ChatGPT, Google BARD, Meta’s LLM) could assist legal professionals in their quest to find information, create documents, and help outline legal processes and practices.
He proposed three ways of thinking about the work being done by these models, which are largely analogous to traditional methods. The first way is what Riehl refers to as a “bullshitter,” where a model generates information without providing citations for the information. The second way is called a “searcher,” where a model generates a legal brief, but does not provide citations, forcing the user to search for support. The third way is called a “researcher,” where the model finds relevant cases and statutes, extracts relevant propositions, and crafts a brief based on them.
Riehl believes that option three, being a researcher, is the most likely to win in the future, as it provides “ground truth” from the start. He cites Fastcase’s acquisition of Judicata as an example of how AI can be used to help with research by providing unique identifiers for every proposition and citation, enabling users to evaluate the credibility of the information. In conclusion, Riehl sees a future where AI is used to help researchers by providing a pick list of the most common propositions and citations, which can then be further evaluated by the researcher.
One thing is very clear, we are just at the beginning of a shift in how the legal industry processes information. Riehl’s one-two combination of SALI Standards combined with additional AI and human capabilities will create a divide amongst the bullshitters, the searchers, and the researchers.

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Continue Reading The Bullshitter, The Searcher, and The Researcher – Damien Riehl on the Dynamic Shift in How the Legal Profession Will Leverage Standards and Artificial Intelligence

“Whether you like it or not, everybody’s searching for us online. And everybody is looking at your LinkedIn profile, whether you’re on LinkedIn every day, or once a year, so you might as well make it work for you.” – Stefanie Marrone
Stefanie Marrone is an Outsource Marketer who advises legal professionals on improving their social media presence. Even legal professionals in large law firms can benefit from a strong social media presence because clients and potential clients relate to the individual more than they do the firm. Marrone’s experience in firms like Proskauer and MoFo helped shaped her understanding of how important it is to have a strategy when it comes to branding. LinkedIn is her suggested primary platform for lawyers and legal professionals because that is the most likely platform where you’ll find your peers and clients.
One of the most effective forms of content, even on LinkedIn, is short-form video. In addition, list posts, infographics, carousel images, and finding ways to bring even firm posts to life helps draw attention to social media posts. For lawyers who have a marketing team, Stefanie suggests establishing a social media training program, especially for LinkedIn.
While we would all love to have some metric that identifies the return on investment of social media, it is not as easy as the number of likes a post receives. Success on social media is a combination of brand awareness, influence on decision making, and information dissemination. However, Marrone points out that many firms have thousands, or even tens of thousands of followers, and if the only engagement you are receiving is minimal, or from a few people, then it is clear that your social media strategy is not working.
Marrone also points out that lawyers and legal professionals should stick to one or two platforms and not spread yourselves too thin. LinkedIn, YouTube, and Twitter are probably the safest bets, but it depends on the message you are trying to convey.

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Continue Reading Successful Brand Awareness for Legal Professionals – Tips from Stefanie Marrone (TGIR Ep. 188)

[Note: Please welcome guest bloggers Jennifer Wondracek, Director of the Law Library, Professor of Legal Research & Writing at Capital University Law School, and Rebecca Rich, Assistant Dean for the Law Library and Technology Services, and Assistant Teaching Professor at Drexel University Thomas R. Kline School of Law. – GL]

AI content generation tools, such as ChatGPT, have been in the news a lot lately. It’s the new cool tool for doing everything from coding to graphic art to writing legal briefs. It was even, briefly, used for a robot lawyer that was going to argue in court. And Greg Lambert wrote about it a few weeks ago on this very blog in What a Law Librarian Does with AI Tools like ChatGPT – Organize and Summarize. This post continues Greg’s discussion on ChatGPT use.

AI content generation tools are also the new education bogeyman. A myriad of headlines have been written in the last two months about how ChatGPT is the death of the essay and multiple-choice exams. It’s the newest in a line of digital tools–starting with the Internet and Wikipedia–that students might use to cheat in legal education. But we think this is a bit of an overreaction. ChatGPT and other similar AI-generative content creation tools can, and have, absolutely been misused; but we found that even with expert prompt creation and a high level of expertise, ChatGPT et al. are not yet capable of producing student work that is indistinguishable from real student work. Not all share this belief. Several professors at the University of Minnesota Law School ran some exams through ChatGPT, using a closed universe of cases provided to the program. The exams resulted in an average grade of C+ across the four exams, which were graded blindly. But they noted a few important takeaways, including:

[W]hen ChatGPT’s essay questions were incorrect, they were dramatically incorrect, often garnering the worst scores in the class. Perhaps not surprisingly, this outcome was particularly likely when essay questions required students to assess or draw upon specific cases, theories, or doctrines that were covered in class.

And

In writing essays, ChatGPT displayed a strong grasp of basic legal rules and had consistently solid organization and composition. However, it struggled to identify relevant issues and often only superficially applied rules to facts as compared to real law students.

The authors of this blog post have also done some experiments with ChatGPT. Jenny was curious about the kind of legal work that ChatGPT thought it could perform. When asked what types of legal tasks it could do, ChatGPT listed seven options, ranging from summarizing laws to drafting legal documents. The option that caught Jenny’s eye was “Helping with legal research, by providing the most relevant cases and statutes.” Challenge accepted.

Using a current problem her Legal Research and Writing students were working with, Jenny asked ChatGPT “What are the most relevant cases and statutes to determine if someone is a recreational user land entrant under Ohio law?” A few seconds later, ChatGPT gave her two statutes and three cases with brief summaries of each. While it had the general premise correct that a landowner is not liable for injury to a recreational user, assuming all of the requirements are met, it provided incorrect definitions, and every statute and case cited were incorrect. It also disagreed with itself about the duty of care owed to the recreational user in another sentence. Neither statute provided led to R.C. 1533.18 or 1533.181, the Ohio statutes for this law. When asked for more citations for the three cases listed, Jenny received both regional reporter and Ohio citations that were readable, if not quite properly Bluebooked. Investigations into the cases determined that none of the three existed by name and each of the six reporter citations led to a unique case, none of which were remotely responsive to the question. In the end, ChatGPT gave Jenny a partially correct answer with two incorrect statutes, three made-up cases, and six incorrect cases. Not a good day for accurate legal research!

Becka experimented with the law-review style research and policy paper prompts she uses for her Education Law and AI and the Law classes and had a similar experience.  Even with prompts to write longer papers, ChatGPT produced short, generically written papers with no or minimal citation (including often made-up citations!) and no analysis.  A five-page paper would have an average of two footnotes per page even when prompted to add more. Becka shared the results with one of her students who commented that even she could tell this was an F paper.  Becka also experimented with having ChatGPT create a class policy presentation. Again, even after several refining prompts, the presentation was, at best, a C- presentation.

Given the legal writing learning curve and low level of longer form writing experience of many of our students, along with their documented increased level of stress and reduced mental health, it is understandable that instructors are nonetheless concerned about the use of AI content generation tools.

As with plagiarism tools, there is now a profitable market for detecting the use of AI generated content using AI.  There are currently at least two startups developing tools: AICheatCheck and CrossPlag, both of which have usable demos. GLTR and GPTZero were developed by a collaboration between an MIT and a Harvard professor and a Princeton University student respectively (for more about these tools and a comparison of how they work, take a look at this RIPS-SIS post). Our friendly neighborhood plagiarism detection companies, Turnitin and Copyleaks, are also in the process of adding AI content generation tool use detection to their products. OpenAI (ChatGPT’s company) has developed a tool to assist in detecting the use of its tool in writing and is in the process of adding watermarking to ChatGPT-generated content.

None of these detection tools are 100% effective, so it may also be helpful to consider adding ChatGPT detection options to your paper grading rubric.  Some options:

  • ChatGPT generated text is formulaic: it generally follows the 5-paragraph essay, stereotypical topic sentence at the top structure.
  • Sentence length does not vary as much as human-generated text does.
  • ChatGPT generated text is light on analysis and applying facts to an issue.

Also remember that ChatGPT isn’t good at citation and doesn’t have any information in it from after 2021 yet. Well-done, indistinguishable from humans is a difficult enough problem to solve that no one’s gotten there yet (though an Israeli start-up is trying).

Lastly, we recommend considering teaching students about ChatGPT rather than banning it.  There are so many AI-assisted drafting tools available for lawyers now that we’d be doing them a disservice otherwise (e.g. ClearBrief, Clawdia, and Docugami). The Sentient Syllabus Project has three great principles for doing so:

  1. AI cannot pass this class,
  2. AI contributions must be limited and true, and
  3. AI use should be open and documented.

On to the next experiment!

 

This week we are joined by Shayne Phillips, Director of Analytics Solutions at Anaqua/Acclaim IP. Shayne talks about the value that Patent Agents bring to the legal industry. She explains how Patent Agents can use data to uncover insights about competitors and potential markets, and how they can go beyond the typical patent search to provide competitive intelligence and business intelligence.
She emphasizes how Patent Agents can help R&D teams use the patent literature to their advantage, such as looking for references to answer an office action from an examiner or to conduct a freedom to operate opinion piece. Additionally, Patent Agents can look for trends in what patents a competitor has let die before their statutory term, and what countries they are now filing in where they maybe hadn’t before with that particular technology. This can help uncover potential markets and provide insight into what the competitor may or may not know about a particular geographical region.
Many law firms realize the value that Patent Agents can bring to making existing clients more profitable by understanding their patent portfolio as well as uncovering the strategic directions that potential clients may be headed in their patent and overall business goals.
As with many industries, Shayne recognizes that there is a giant role that technology and AI tools will play in the immediate future of the profession. With millions upon millions of patents to parse through, there is definitely value in leveraging the technology to enhance the role she plays in finding the hidden jewels that are buried in patent information.
Join us as we talk with Shayne about her career growth, her move to Texas, and her involvement in the Licensing Executive Society to mentor and connect with the newer generation of LES members.
Shayne’s Twitter – @ShaynePhillip15

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Continue Reading The Secret Weapon: Leveraging Patent Agents to Gain a Competitive Edge – Shayne Phillips (TGIR Ep. 186)

AI Generated Librarian as a machine editing a podcast.

This is going to be something that all of you will find “interesting,” but maybe not something that you will like. Last week on 3 Geeks, I posted a blog that talked about how to use AI to generate summaries of legal articles. This week, I wanted to expand on that project a little and see if I could turn the summaries into a podcast. The goal was to try to get it completely automated, and completely AI generated. Well… as you can see from the title of this episode, it was almost completely automated, and AI generated. But not 100%.

Here’s the process I created, and I attached the mp3 of the output.
  • RSS Feed that tracks new BigLaw Podcast Episodes.
  • Use a Python script to pull the episode information.
  • Use GPT to create a description of the episode.
  • Use Descript to translate the text summaries into voice output. (I did lightly edit these with an intro and outro as well as tweak the transitions between each review.)
  • Use Soundraw to create an intro/outro music.
  • Combine in Audacity.
  • Output in mp3.
All of these tools are actually free, except for GPT, which is about $0.01 or less per article.
This is far from perfect, but it is kind of cool, and I think there are some uses for these tools.
Whether you love this, hate this, or don’t really care, I’d like to hear what you think!!
(The AI Generated part starts at the 6:50 mark.)

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Continue Reading The (ALMOST) Completely AI Generated Podcast (TGIR Ep. 186)

DALL-E drawing of a librarian looking over lots of documents.

There is obviously a ton of hype and buzz going on right now with ChatGPT and other AI tools, including this week’s Geek in Review podcast. I wanted to see if there’s something that I could do that was a practical use of GPT in my job as a law librarian. I think I’ve found something that might fit that bill. Summarizing text.

Law Librarians are great at finding good information and getting that quickly into the hands of lawyers, legal professionals, judges, pro se representatives, etc. However, we don’t always have a lot of time to read all of that information and create a summary for the person we are working with. It’s not uncommon for a firm to have 100 – 300 attorneys for each librarian. Any tool that would help librarians synthesize information in a useful way is a welcome tool for us all. I put GPT 3.5 (the paid version) to the test to see how it could be that electronic assistant in summarizing information quickly.

It is early in my experiment, but I’m impressed with what I’ve seen so far.

The Current Process

I wanted to try something that I personally set up for myself that is “good” but not “great.” And that is tracking BigLaw Podcasts as they come out. What I have now is an RSS feed (yes, that is still a thing!) that follows AmLaw 100/200 firms’ websites and lets me know when a new episode comes out. I have that RSS feed set up in my MS Outlook folders. I’m using LexisNexis’ NewsDesk to set this up.

Right now, it looks like this:

This works fine, but it really doesn’t give me a lot of information on the podcast. I’d really like to see more of a summary of the podcast before I make a decision to click through and listen.

The Idea

I’ve got the basic information from the RSS feed, but now I want to expand that information. I’m a former programmer from “back in the day” but I haven’t done any serious programming in a long time. But, I know that Python is a great tool for processing text, so my top-of-the-head idea was to have Python look at my RSS output and see if it could get me more information. Actually, I wanted to see if Python might be able to summarize the RSS information directly. This is where the ChatGPT tool came in handy. Continue Reading What a Law Librarian Does with AI Tools like ChatGPT – Organize and Summarize

There is a lot of buzz around ChatGPT and GPT 3.5, but is it really the next Tesla, or is it the next IBM Watson? We talk with HyperDraft’s Tony Thai and Ashley Carlisle about OpenAI’s popular tool and why, lawyers at least, shouldn’t be ready to go all in on this specific technology. While there are great examples of how GPT 3.5 impressively handled things like Bar Exam questions, there are still a lot of unknowns from this resource from a company that started out as Open Source and non-profit, but has released a product that is neither.
While the conversation focused a lot on the short comings of ChatGPT, there is a lot of promise in the technology, even if it may be years before it can handle the complex issues that lawyers and the legal community handle on behalf of their clients. Are we going to reach The Singularity in 2023, or is it decades away? Can AI plug the Access to Justice gap, or will it cause more issues than it solves? Will this specific AI tool continue to improve as it devours more data and leverages millions of users, or will it become corrupted by bad actors who discover how it inputs its data?
Can society use this to better ourselves, or will it become another way to play upon our short attention spans?
We cover all of this and more in a roundtable discussion. We’d love to hear your thoughts on what value you see in ChatGPT and GPT 3.5 in the legal industry. So reach out to us on Twitter or give us a call!

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Transcript 

Continue Reading ChatGPT – If It Sounds Too Good To Be True… – Tony Thai and Ashley Carlisle (TGIR Ep. 185)