ChatGPT-4 Can See Us Now! And Our Desiccated Potatoes…

Within the rapidly advancing realm of generative AI, ChatGPT is expanding its inventory of human senses available for its GPT-4 subscribers. For a mere $20 a month, you can experience its new ability to see, speak, and hear! One of these senses, the “vision” enhancement, allows the user to upload images which ChatGPT can analyize and answer questions about. Currently, YouTube is abuzz with endless creative demonstrations of this technology – solving math problems, calculating food calories in a shopping cart, identifying landmarks in photos, reading CT scans, turning sketches into art, creating fashion designs, tracking wildlife, aging or de-aging faces, detecting plant diseases, restoring old photos, suggesting interior design changes, and analyzing aerial images for urban development and natural disasters. Multiple videos are highlighting an intriguing paper, “The Dawn of LMMs: Preliminary Explorations with GPT-4V(ision),” which explores over 100 potential applications of this feature, examining how well the system performs for each.

If you don’t subscribe to ChatGPT-4, you can still check out the image analysis concept using the free open source LLaVA: Large Language Model Vision Assistant.

Originally for this blog post, I was going to examine a few examples of possible uses of this technology for legal practice. However, I got bogged down by my first example, analyzing evidence in photographs, because ChatGPT had so much to say about the image I uploaded! So, I’m focusing solely on that one.

Photograph Analysis

Photographs obviously play a huge role in legal practice. Lawyers, prosecutors, jurors, and others often rely heavily on photographs to understand events and assign blame – be it a photograph from a car accident, a crime scene, or even the positioning of a car in a parking violation scenario. Of course, in crime dramas and true crime documentaries, they either have a brilliant detective who identifies the key piece of evidence in the picture, or it is crowdsourced by the public when it appears in the news. Now, with ChatGPT’s newfound ability to “see,” it’s essentially doing its own form of crowdsourcing. Using the wisdom of countless human eyes contained in its massive training data, ChatGPT-4 can assist and provide insights that an individual’s human eyes can miss.

Example – Analysis of Office Shelf Photograph

To test its abilities, I snapped a goofy photo of a shelf in my office and quizzed ChatGPT about the things it spotted. I was pretty impressed by its descriptive abilities and knowledge about some of the items. Exploring its possible uses for like, the police, I pretended the picture was taken in the home of an international assassin, and then asked for some clues about the assassin. I think that it had some interesting ideas!

I uploaded the image and asked away!

Me: Hi! Please list the items you recognize in this picture.

ChatGPT: Sure! Here are the items I recognize in the picture:

  1. A green piñata (or decorative item) made of fringed paper.
  2. A figurine of an animal, possibly a kangaroo or wallaby, holding red objects (perhaps apples).
  3. A piece of wood or nut with googly eyes attached, resembling a face with an open mouth.
  4. Two glass or ceramic bottles with decorative paintings or inscriptions.
  5. A candy cane or similarly striped stick.
  6. A wooden hand sculpture.
  7. A tray or plate with a pink border.
  8. Various stickers or decals on a gray surface, including ones that say “Night Owl”, “Juicy”, and others.

Let me know if you need more information on any of these items!

Me: Great! You see the little bottle with the nature scene on it? Tell me what you know about that picture.

ChatGPT: Certainly! The bottle you’re referring to appears to be a type of “snuff bottle.” Snuff bottles are small bottles which were primarily used in ancient China to hold powdered tobacco (snuff). These bottles became popular during the Qing Dynasty. The use of snuff was a fashion among the Chinese during this period, and they liked to carry it in small bottles like the one in your picture…. (Note: It continued for 3 more paragraphs.)

Me: Can you read the Chinese characters on the bottle?

ChatGPT: (Note: No, too small, but could read it if I uploaded another picture.)

Me: If I told you that the brown thing with eyes is a dessicated potato, could you take a guess at how long it has been drying out?

ChatGPT: While I can provide a general overview based on the image and your description, it’s important to note that accurately determining the exact duration of desiccation for a potato based solely on a photograph is challenging.

That said, when a potato dries out, it loses its moisture and shrinks, and the skin can become wrinkled and tough. Over time, as the potato continues to dry, it may become lighter in weight and more shriveled in appearance. The potato in the image does appear quite shriveled and dried out.

However, the exact duration of desiccation can vary based on several factors, including.. (Note: It goes on with a list of factors.)

Me: Yeah, that was a good guess! I accidentally left it in my office when I went to the AALL conference in July, and it is October 18 now.

ChatGPT: Ah, that makes sense! Leaving a potato in an environment like an office for several months would certainly lead to the kind of desiccation observed in the image. The absence of moisture and a relatively stable environment would have contributed to

its drying out. It’s interesting to see how everyday items can transform over time when left in certain conditions. I hope your AALL conference went well, and thanks for sharing this intriguing observation with me!

Me: Yeah, I was surprised at how it looked after a week! Can you tell what that is in the potato’s mouth?

ChatGPT: (No, didn’t guess correctly.)

Me: What if I gave you a hint? The tooth fairy.

ChatGPT: Ah, with that hint in mind, it seems the object in the potato’s mouth could represent a “tooth”! Perhaps it’s a playful representation of the potato losing a tooth and waiting for the tooth fairy. In that context, the object could be made of a variety of materials resembling a tooth, such as a piece of white foam, plastic, or even a small piece of chalk or stone. It’s a fun and creative idea!

Me: Actually, one day I was looking at the potato, and I saw one of my daughter’s baby teeth on the shelf! I don’t know when I put it there, but I do remember finding one in my wallet and thinking that I should hold on to it. So, I assume I stuck it on my shelf for safekeeping.

ChatGPT: That’s both surprising and heartwarming! Finding personal mementos in unexpected places can bring back a rush of memories and emotions. Baby teeth are often cherished keepsakes for many parents, reminding them of the fleeting nature of childhood. Your discovery adds a whimsical twist to the tradition of the tooth fairy! The potato unintentionally became a guardian for your daughter’s baby tooth. It’s a unique and memorable story you can share with her in the future.

Me: If this picture would taken by the police at the home of, say, an international assassin, would it give us any clues about the assassin?

GhatGPT: While it’s fun to imagine scenarios, it’s essential to approach such questions with a good dose of caution and skepticism. Using the image you provided, let’s hypothesize in a fictional context:

If the image were taken at the home of an “international assassin” in a crime novel or movie, investigators might look for clues in the following ways:

  1. Personal Touches: The dessicated potato with a baby tooth could suggest a sentimental side or a connection to family. The tooth might indicate the assassin has a child or keepsakes from their past.
  2. Cultural Clues: The snuff bottle with Chinese characters could hint at a connection to China, either personally, professionally, or as a destination of interest.
  3. Hobbies or Interests: The various trinkets and decorations might suggest that the assassin has an interest in art, culture, or collecting unique items.
  4. Stickers and Decals: The stickers on the gray surface might provide clues about places they’ve visited, brands they like, or organizations they support or are involved in.
  5. Behavioral Clues: The fact that the potato was left to desiccate for months might indicate a person who travels frequently or isn’t often at home.
  6. Potential Disguises: Items like the piñata could be tools for blending in at parties or social events.

However, it’s essential to note that these are purely speculative interpretations based on a fictional scenario. In a real-world context, such deductions would be highly speculative and could lead to false conclusions. Personal items in someone’s home often reflect their tastes, memories, and everyday life rather than their profession, especially one as clandestine as an “international assassin.”

Me: Thanks for your help today!

Generative AI in the Legal Sphere

Well, that was definitely interesting! Then, naturally, I turned to ChatGPT to explore potential applications in the legal sector, and it had endless ideas. Here are but a few:

Generative AI, especially with its visual capabilities, offers a plethora of applications in the legal sector:

  • Document Review: Beyond just text, AI can now scan and interpret visual elements in legal documents, helping in tasks like verifying signatures or checking seals.
  • Evidence Analysis: AI can be used to enhance low-quality surveillance footage, identify faces in crowded scenes, or even analyze patterns in digital data visualizations.
  • Contract Visualization: Instead of lengthy text, visualize contract terms as diagrams or flowcharts, making them more digestible for clients.
  • Property Assessment: Using satellite imagery, AI can assess property boundaries, land usage, or even environmental compliance.
  • Accident Reconstructions: AI could recreate accident scenes in 3D based on photos and other evidence, aiding in personal injury or insurance cases.
  • Copyright Compliance: Scan vast repositories of images to identify potential copyright violations or unauthorized use of intellectual property.
  • Client Interactions: Use AI to generate visual explanations of complex legal scenarios, helping clients grasp intricate details through interactive diagrams or simulations.
  • Forensic Art: Create composites or age-progression photos in missing person cases or help juries visualize crime scenes.
  • Legal Research: AI can scan through visually rich documents like patents, identifying key diagrams or illustrations that could be crucial in patent litigation.”

Conclusion

With tools like ChatGPT expanding their capabilities, the intersection of AI and law is clear. ChatGPT’s new features highlight potential efficiencies and improvements for legal processes!

Crystal Ballalytics: Judicial Behavioral Forecasting Modeling

The trifecta of big data, advanced analytics, and recent AI innovations is ushering in a new era of judicial analytic mind-reading, enabling software to more accurately predict judges’ court rulings. Last year, in what seems like an interesting leap forward in judicial analytics, Pre/Dicta unveiled its AI-powered litigation prediction software, introducing a novel, perhaps radical, approach to tapping into the judicial mind. According to CEO Dan Rabinowitz, Pre/Dicta is the only litigation analytics platform that makes verifiable predictions about the outcome of lawsuits. He claims that using data science and only a docket number, Pre/Dicta’s software correctly forecasts how judges will decide on motions to dismiss 86% of the time, without factoring in the specific facts of the case. The system covers civil litigation cases at both the state and federal level, but does not attempt to forecast results of jury trials.

Rather than solely depending on a judge’s past rulings and jurisprudence, as is common with other judicial analytics products, Pre/Dicta uses a methodology similar to that used in targeted advertising. This approach forecasts future behavior by examining both past actions, such as purchasing habits, and individual biographical characteristics. Pre/Dicta works by combining historical ruling data with biographical and demographic details to forecast a judge’s decision in a given case. Using around 120 data points, it spots patterns and potential biases in a judge’s past rulings. The system evaluates specifics of past rulings, considering elements such as the nature of the case (e.g., securities fraud, employment discrimination), the attorneys and firms involved (e.g., solo practitioner representing an individual, regional firm representing a corporation, AmLaw 100 firm backing an individual), and the nature of the disputing parties (e.g., individual vs. corporation, small company vs. large corporation). This case-specific information is then combined with the judge’s personal data, like net worth, political affiliations, professional history, and law school alma mater, to generate a prediction.

Prediction in the Legal Landscape

86% accuracy is impressive! Hopefully, Pre/Dicta will spark a judicial prediction analytics arms race. According to Daniel L. Chen in his article, “Judicial Analytics and the Great Transformation of American Law,” predictive judicial analytics “holds the promise of increasing the efficiency and fairness of law.” Targeted advertising seems to work pretty well, so hopefully Pre/Dicta’s advancements in this area is a positive step toward making the judicial process more transparent.

If only we knew what would happen in the future, we would know what to do now! For as long as there have been courts and judges, folks have tried to predict whether a judge would rule in their favor. Attorneys have always engaged in mental “judicial analytics” by gathering and pondering information on a judge’s past rulings and reputation to glean some insights into how they might decide a case. Humans are prediction machines, given our innate tendency to draw on experiences and knowledge to anticipate events—an evolutionarily useful skill that allowed us to sometimes escape being saber-toothed tiger lunch or the victim of grumpy neighboring tribal predations.

From my brief stint practicing family law in the 1990s, I discovered that family law clients are hopeful individuals. Despite clear child support guidelines and a prevailing judicial preference for shared custody, people often believed that if a judge merely heard the specifics of their “special snowflake” scenario involvinga cheating spouse or a deadbeat dad, the judge would surely deviate from the rules and customary practices to grant them a deserved favorable ruling. They struggled to accept that judges could be indifferent to their parade of marital/parental horribles. And even if judges were initially inclined to empathize, after many years of sifting through outright lies and half-truths, they had seemingly given up on given up on deciphering reality anyway. It was always challenging to persuade clients of the judicial propensity to metaphorically split the baby down the middle, whether financially or custodially.

Attorneys have needed to hone their abilities to predict outcomes so they could counsel their clients on different courses of action. While making no promises, they share predictions regarding claim values, the odds of surviving summary judgment, potential jail sentences, the likelihood of obtaining sole custody of children, and so on. Attorneys can only do so much, though. Hopefully, as predictive judicial analytics tools improve and become widely available, they have the potential to promote fairness, cut down on litigation costs, and create a more transparent and predictable judicial system.

Judicial Behavioral Forecasting Modeling

Certainly, judges do provide clients with information that assists in anticipating how a ruling might unfold. I have observed numerous judges delivering impactful speeches during temporary hearings, highlighting the importance of adhering to child support guidelines and the principle of shared custody. When clients receive information regarding a likely outcome, their acceptance of reality accelerates significantly. It would indeed be beneficial, and save a lot of time, money, and anguish, if a client could engage in a comprehensive discussion with a judge, probing various questions about how different pieces of information might influence their ruling. However, this isn’t the modus operandi of judges, as they cannot communicate with one party in a suit independently, nor do they pre-announce their rulings prior to a hearing or trial. Now, however, companies like Pre/Dicta are leveraging the human trait of predictability inherent in judges. Like everyone, judges have their own set of ideas, habits, preferences, prejudices, and temperaments shaped by a mix of genetics and experiences, all of which contribute to a certain level of predictability in their rulings.

Hopefully, soon we will be able to pick the mind of a judge without the necessity of actually speaking with her. With the advancing tide of artificial intelligence and the ongoing proliferation and refinement of judicial analytics products, it seems plausible that the future might produce a family law judge behavioral forecasting model for specific judges. These models could help attorneys and their clients identify potential biases of judges. They could see how a judge might respond to a person based on certain characteristics like sex, race, age, income, profession, or criminal history, especially when compared to another party with a different background. Also, if these models included information about factors that affected past rulings, they could be used to anticipate how certain situations might be viewed by the court. For example, a parent hoping to keep their soon-to-be ex-spouse away from the kids might want to know if the judge objects to stuff like dating app addiction, not taking the child to piano lessons, or multiple DUIs arrests. Armed with information, they can choose the best way to handle their case, including deciding if going to trial is a good idea.

Behavioral forecasting models are of course not new to law and legal practice. They are tools used to predict the likely behaviors of individuals or groups across various domains, aiding in better decision-making. In the legal sector, in addition to predicting the outcome of Supreme Court cases, they aid in litigation strategy, legal analytics, resource allocation, criminal behavior prediction, policy impact analysis, legal document analysis, dispute resolution, and regulatory compliance, by leveraging historical data and legal precedents to inform decision-making and strategy development. They are utilized in other fields too like marketing, finance, HR, healthcare, public policy, urban planning, criminal justice, technology, environmental science, and education to forecast behavioral patterns, helping to optimize strategies and allocate resources more efficiently.

Such an innovation would undeniably be a game changer. Clients in divorce and custody disputes might believe solid advice regarding likely outcomes, rather than cling to the hope that their unique case details will influence the judge. Accurate predictions would likely deter individuals from wasting money, and likely be a boon for judges struggling with a backlog of cases. Having these predictive tools on their websites would no doubt promote case settlements and therefore ease some of the strain on both judges and the judicial system.

Naysayers

As always, naysayers abound. Some argue that judicial analytics could undermine the legitimacy of an impartial judiciary. In fact, in France, judges are so wary of transparency that judicial analytics products are prohibited. Well, at least in the U.S., that boat has sailed, far and fast. Particularly in light of Supreme Court rulings in recent years, many people have realized that judges often base their rulings on ideological leanings and personal preferences. Robots would only further confirm what we already suspect – that judges are just like the rest of us with habits, biases, and opinions. It might be too late to rehabilitate the judiciary, but perhaps the transparency of data-driven prediction could bolster public confidence more than frequent affirmations of judicial objectivity.

Then, there are arguments regarding fairness due to cost. For now, the high cost of Pre/Dicta raises potential fairness issues, as only larger firms and wealthier clients can harness its predictive power. True, they always have an advantage. However, as the technology becomes more common, costs should decrease, making it more and more accessible.

Conclusion

The improvement of AI-driven judicial analytics, exemplified by Pre/Dicta, could mark a revolutionary shift in the legal realm, perhaps promising a new level of predictability and transparency in court outcomes. While concerns about fairness, accessibility, and the perception of judicial impartiality persist, the potential benefits—reduced litigation costs, enhanced transparency, and more informed decision-making—may herald a future where data-driven insights guide legal strategy and expectations. As technology continues to evolve and become more accessible, the future looks promising for both practitioners and those seeking justice.

AI’s Mechanical Jurisprudence

Guest post by Nicholas Mignanelli, Research Librarian, Yale Law School

In his 1908 essay, “Mechanical Jurisprudence,” the eminent legal scholar Roscoe Pound warns of the dangers of what he calls “scientific law,” namely a “petrification” that “tends to cut off individual initiative in the future, to stifle independent consideration of new problems and of new phases of old problems, and so to impose the ideas of one generation upon the other.” Today, this century-old critique of legal formalism could be used to describe the pitfalls of so-called “AI-driven” legal research and law practice technologies.

Pound’s early work served as the foundation for legal realism, an intellectual movement that radically transformed American law by exposing the human element in judicial decision-making and introducing the indeterminacy thesis—the idea that “laws (broadly defined to include cases, regulations, statutes, constitutional provisions, and other legal materials) do not determine legal outcomes.” Unfortunately, the insights of the legal realists are lost on the founders of today’s legal tech startups and their promoters, even those within the legal academy. As Upton Sinclair once wrote, “It is difficult to get a man to understand something when his salary depends on his not understanding it.”

Yet foundational questions abound. Is law determinate? What systemic biases and hidden assumptions are embedded in the corpus of Anglo-American law? What are the implications of turning the corpus of Anglo-American law into a dataset and automating it? Will AI inhibit the legal creativity exemplified by lawyers like Thurgood Marshall and Ruth Bader Ginsburg? What will all of this mean for the future of law reform? While we can hardly expect vendors to take time to reflect upon these questions, law librarians, in their roles as legal research professors and legal information scholars, must.

Further Reading

Ronald E. Wheeler, Does WestlawNext Really Change Everything: The Implications of WestlawNext on Legal Research, 103 Law Libr. J. 359 (2011).

Susan Nevelow Mart, The Algorithm as a Human Artifact: Implications for Legal [Re]Search, 109 Law Libr. J. 387 (2017).

Susan Nevelow Mart, Results May Vary, A.B.A. J., Mar. 2018, at 48.

Nicholas Mignanelli, Critical Legal Research: Who Needs It?, 112 Law Libr. J. 327 (2020).

Yasmin Sokkar Harker, Invisible Hands and the Triple (Quadruple?) Helix Dilemma: Helping Students Free Their Minds, 101 B.U. L. Rev. Online 17 (2021).

Nicholas Mignanelli, Prophets for an Algorithmic Age, 101 B.U. Law Rev. 41 (2021).

Stephan Meder, Legal Machines: Of Subsumption Automata, Artificial Intelligence, And the Search for the “Correct” Judgment (Verena Beck trans., 2023).

Gesundheit ChatGPT! Flu Snot Prompting?

Somewhat recently, during a webinar on generative AI, when the speaker Joe Regalia mentioned “flu snot” prompting, I was momentarily confused. What was that? Flu shot? Flu snot? I rewound a couple of times until I figured out he was saying “few shot” prompting. Looking for some examples of few-shot learning in the legal research/writing context, I Googled around and found his excellent article entitled ChatGPT and Legal Writing: The Perfect Union on the write.law website.

What Exactly is Few Shot Prompting?

It turns out that few-shot prompting is a technique for improving the performance of chatbots like ChatGPT by supplying a small set of examples (a few!) to guide its answers. This involves offering the AI several prompts with corresponding ideal responses, allowing it to generate more targeted and customized outputs. The purpose of this approach is to provide ChatGPT (or other generative AI) with explicit examples that reflect your desired tone, style, or level of detail.

Legal Research/Writing Prompting Advice from write.law

To learn more, I turned to Regalia’s detailed article which provides his comprehensive insights into legal research/writing prompts and illuminates various prompting strategies, including:

Zero Shot Learning/Prompting

This pertains to a language model’s ability to tackle a novel task, relying on its linguistic comprehension and pre-training insights. GPT excels in zero-shot tasks, attributed to its robust capabilities. (Perhaps unsurprisingly, one-shot learning involves providing the system with just one example.)

Few-Shot Learning/Prompting

Few-shot learning involves feeding GPT several illustrative prompts and responses that echo your desired output. These guiding examples wield more influence than mere parameters because they offer GPT a clear directive of your expectations. Even a singular example can be transformative in guiding its responses.

As an example of few-shot learning, he explains that if you want ChatGPT to improve verbs in your sentence, you can supply a few examples in a prompt like the following:

My sentence: The court issued a ruling on the motion.Better sentence: The court ruled on the motion. 
My sentence: The deadline was not met by the lawyers. 
Better sentence: The lawyers missed the deadline. 
My sentence: The court’s ruling is not released. [now enter the sentence you actually want to improve, hit enter, and GPT will take over]
[GPT’s response] Better sentence: The court has not ruled yet [usually a much-improved version, but you may need to follow up with GPT a few times to get great results like this]

And Much More Prompting Advice!

Regalia’s website offers an abundance of insights as you can see from the extensive list of topics covered in his article. Get background information on how geneative AI system operate, and dive into subjects like chain of thought prompting, assigning roles to ChatGPT, using parameters, and much more.

  • What Legal Writers Need to Know About GPT
  • Chat GPT’s Strengths Out of the Box
  • Chat GPTs Current Weaknesses and Limitations
  • Getting Started with Chat GPT
  • Prompt Engineering for Legal Writers
  • Legal Writing Prompts You Can Use with GPT
  • Using GPT to Improve Your Writing
  • More GPT Legal Writing Examples for Inspiration
  • Key GPT Terms to Know
  • Final Thoughts for GPT and Legal Writers

Experimenting With Few-Shot Prompting Before I Knew the Name

Back in June 2023, I first started dabbling in few-shot prompting without even knowing it had a name, after I came across a Forbes article titled Train ChatGPT To Write Like You In 5 Easy Steps. Intrigued, I wondered if I could use this technique to easily generate a profusion of blog posts in my own personal writing style!!

I followed the article’s instructions, copying and pasting a few of my favorite blog posts into ChatGPT to show it the tone and patterns in my writing that I wanted it to emulate. The result was interesting, but in my humble opinion, the chatty chatbot failed to pick up on my convoluted conversational (and to me, rather humorous) approach. They say that getting good results from generative AI is an iterative process, so I repeatedly tried to convey that I am funny using a paragraph from a blog post:

  • Prompt: Further information. I try to be funny. Here is an example: During a text exchange with my sister complaining about our family traits, I unthinkingly quipped, “You can’t take the I out of inertia.” Lurching sideways in my chair, I excitedly wondered if this was only an appropriate new motto for the imaginary Gotschall family crest, or whether I had finally spontaneously coined a new pithy saying!? Many times have I Googled, hoping in vain, and vainly hoping, to have hit upon a word combo unheard of in Internet history and clever/pithy enough to be considered a saying, only to find that there’s nothing new under the virtual sun.

Fail! Sadly, my efforts were to no avail, it just didn’t sound much like me… (However, that didn’t stop me from asking ChatGPT to write a conclusion for this blog post!)

Conclusion

For those keen to delve deeper into the intricacies of legal research, writing, and the intersection with AI, checking out the resources on write.law is a must. The platform offers a wealth of information, expert insights, and practical advice that can be immensely valuable for both novices and seasoned professionals.

A Blog Was Born (Or A Blog Arises from the Ashes of Forsaken Blogs)

Welcome to the inaugural post of AI Law Librarians! This blog aims to delve into the intersections of generative AI, law librarianship, legal research, legal technology, legal education, teaching legal research, and perhaps other TBD topics.

The origin story of this blog is a tale as old as time – good intentions smacked up against reality and inertia. Excited about our new chatty chatbot friends that began proliferating at the end of 2022, all of us (Sarah and Sean individually, and Jenny, Becka, and Rebecca collectively) thought to ourselves, “Self (“selves” in the case of the trio), I/we should start a blog about generative AI from the law librarian perspective! The initial enthusiasm was real – we each jumped on creating sites or/and snapping up domain names. However, we each soon realized it would be a lot of work, and our initial efforts never really took flight, in fact, the fledgling fell out of the nest into a caldera of molten lava – until now!

Sarah

My own little slice of the story is that, after several years as a RIPS Law Librarian Blog contributor, I was suddenly out of things to say, so I resigned in 2021. However, when ChatGPT-3.5 hit the scene at the end of 2022, I was so taken with the new technology that I started to have, like…thoughts again and began to barrage the editor with unsolicited ChatGPT guest posts.

Finally, after years of anticipation harking back to the days of The Flintstones, my robot companion had finally arrived—or at least a version of it. And as the excitement/apprehension in the media grew, it became evident that interest in generative AI was going to survive beyond the usual hype cycle. This new technology wasn’t merely a chatbot for amusement, it was poised to remake (erm… or destroy) the world as we know it and revolutionize everything, including law practice and education. Suspecting that the RIPS Law Librarian Blog editor was tiring of my unsolicited musings, I considered starting my own blog and even went as far as creating a WordPress site, only to quickly abandon the idea after deciding it would be too much work.

I will leave Sean his own slice of the origin story, but we had previously discussed the lack of outlets for law librarian blog posts about ChatGPT et al. After reading a particularly informative guest blog post by him on the RIPS-SIS blog entitled The Case for LLM Optimism, I emailed to throw out the idea of starting our own blog. To my mild surprise, he readily agreed (sun devils and wildcats unite!), and we searched our minds for a domain name and some like-minded folks to join us on this unprofitable adventure, and shortly thereafter, AI Law Librarians was born.

Sean

In my journey to the blog, I recently penned a couple of articles I posted on SSRN about fine-tuning LLMs for legal practice and the innovative use of chatbots by law students, and a couple of blog posts which had no obvious home in law library publications. Like Sarah, I didn’t want to swamp the RIPS-SIS blog editor with guest posts, and I had the idea of starting my old blog. As I was planning to escape the Arizona desert for a new job as the Director of Technology Innovation in Oklahoma, Sarah emailed me and suggested we start our own blog, and I was like, “Let’s get on Zoom today!” The brainstorming for domain names was brief—Sarah had a few ideas, and then “ailawlibrarians.com” popped into our minds. We pondered who to ask to join our endeavor for a few days before deciding on Rebecca, who had seemed interested in AI at the LIT-SIS roundtable in Boston.

Rebecca

I have been scrambling since November 2022 to understand this new technology and how it will affect legal research and writing. Several months ago, I connected with Jenny and Becka, and we have presented several times over the summer (together and separately) on AI and the law, mostly to faculty and attorneys. Then Sarah and Sean approached me just as the summer was coming to a conclusion and I realized that despite wanted to write on these topics these summer, I hadn’t actually *published* anything. I had a few blog posts in the hopper, so it was perfect timing. I shared with them that a couple of librarian colleagues, Jenny and Becka, and I had begun our own blog venture with the name chatgptandfriends.com, which was currently stalled after a “Hello World” WordPress post. But then, an idea emerged – why don’t the five of us combine efforts?  Sarah and Sean were excited by the prospect of getting three people by asking just one, and Jenny and Becka agreed. 

Becka

I had taught AI and the Law twice when generative AI became something that everyone knew and was worried about. The more I read about generative AI, the more concerned I became – not because I was worried about academic misconduct or cheating, but because I was worried that law faculty and law librarians would swing the teaching pendulum so far back in the other direction that it would harm some of our most vulnerable students. There were legal issues, ethics issues, pedagogy issues, and places where AI intersected with the other courses I teach: Education Law and Disability Law. Jenny and I talked about how to get the word about the learning positive and negative uses of AI out there and then Jenny brought in Rebecca. The three of us have been talking for months and I’ve also been talking with my university’s committee on generative AI and the law school’s. When Rebecca heard from Sarah, it made sense for all of us to get together.

Jenny

As Becka noted, I like generative AI.  My origin story is not nearly as exciting as some…”I teach law practice technology, so I better check this thing out…well, this is going to cause a fuss when people don’t realize it hallucinates!”