Articles and Columns for October 2023 Research Guide: Law of Armed Conflict – In this period of wild claims and counterclaims, the subject of the Law of Armed Conflict, (LOAC), often referred to as International Humanitarian Law (IHL) or the Law of War (LOW), is a timely subject. Researching LOAC can challenge U.S. researchers and …
Some recent headlines have reported disturbing news about respected and respectable scholars falsifying or just ignoring data conclusions in scholarly papers. This is another example of the skepticism many of us have with the shifts in misinformation flooding our inboxes and newsfeeds, compelling each of us to exercise our critical thinking skills. And the examples we’re referring to aren’t even results of AI. It is human error, strong bias at play, or manipulative intention for one purpose or another. This leads us to another topic in our continuing explorations of human motivation. Why do we lie? Why do we cheat? Kevin Novak takes a deeper dive on this discussion about the issues and the people and actions that have been in the news recently.
Whether speaking with lawyers and law students who haven’t gotten around to trying ChatGPT or collaborating with post-doc explainable and legal AI experts with 20+ years of machine learning and Natural Language Processing experience, Colin Lachance, legal tech innovator and leader, is no closer to understanding in what way and precisely when permanent change will come, but is unshakeably convinced that change will be enormous, uneven, disruptive and, in many cases, invisible.
Jocelyn Stilwell-Tong, Law Librarian, California Court of Appeal, Sixth Appellate District, has determined that although free AI online is useful, the developing products from major legal research platforms show great promise. These paid products control for issues like hallucinations, and provide citations supporting their work so a researcher can confirm the accuracy and context of the materials the AI is pulling from. Issues surrounding data governance (what the company does with your uploaded material and search history) can be controlled by contract, and the legal vendors understand that this is a concern for most legal clients.
Articles and Columns for September 2023 Adding a ‘Group Advisory Layer’ to Your Use of Generative AI Tools Through Structured Prompting: The G-A-L Method – The emergence of Large Language Models (LLMs) in legal research signifies a transformative shift – Dennis Kennedy asks us to Imagine a world where expert advice is at your fingertips, instantly …
Dennis Kennedy asks us to Imagine a world where expert advice is at your fingertips, instantly available, tailored just for you. Think of a tool that’s always ready to give expert advice, without the need for complex coding or tech skills. The Group Advisory Layer Method (G-A-L Method™) revolutionizes decision-making by merging traditional principles of mastermind groups and advisory boards with the cutting-edge capabilities of generative AI. Traditional advisory boards, often hindered by logistics and time constraints, meet their match as the G-A-L Method offers on-demand, diverse, and tailored insights, all without the real-world hassle. It’s like having a virtual team you can chat with any time, made up of tireless AI-created ‘personas’ that act like real people. Instead of juggling schedules or waiting for feedback, you get quick and practical tips from this always-on expert team. The G-A-L Method pioneers dynamic group interactions using personas to give you practical, just-in-time expert advice. What’s more, it makes sure real people (like you) are involved where they add the most value. With the G-A-L Method, you’re not just listening to machines – you’re teaming up with them. This white paper by Dennis Kennedy, well-known legal tech and innovation advisor, law professor, infotech lawyer, professional speaker, author, and podcaster, is an invitation to unlock the untapped potential of these generative AI tools in a practical, structured way to move your efforts forward. Kennedy states that we are poised at the brink of a transformative era where informed decisions can be made rapidly and confidently. The G-A-L Method is more than a technique—it’s a game-changer.
The pace of generative AI development (and hype) over the past year has been intense, and difficult even for us experienced librarians, masters of information that we are, to follow. Not only is there a constant stream of new products, but also new academic papers, blog posts, newsletters, and more, from people evaluating, experimenting with, and critiquing those products. With that in mind, Rebecca Fordon shares her favorites, as well as recommendations from her co-bloggers.
Professor Aarushi Bhandari was taken aback when she learned that not a single student had heard that the Writers Guild of America had reached a deal with the Alliance of Motion Picture and Television Producers, or AMPTP, after a nearly 150-day strike. This historic deal includes significant raises, improvements in health care and pension support, and – unique to our times – protections against the use of artificial intelligence to write screenplays. Across online media platforms, the WGA announcement on Sept. 24, 2023, ended up buried under headlines and posts about the celebrity duo of Taylor Swift and Chiefs tight end Travis Kelce. To Bhandari, this disconnect felt like a microcosm of the entire online media ecosystem.
Hallucinations in generative AI are not a new topic. If you watch the news at all (or read the front page of the New York Times), you’ve heard of the two New York attorneys who used ChatGPT to create fake cases entire cases and then submitted them to the court. After that case, which resulted in a media frenzy and (somewhat mild) court sanctions, many attorneys are wary of using generative AI for legal research. But vendors are working to limit hallucinations and increase trust. And some legal tasks are less affected by hallucinations. Law Librarian and attorney Rebecca Fordon guides us to an understanding of how and why hallucinations occur and how we can effectively evaluate new products and identify lower-risk uses.
Human factors engineer James Intriligator makes a clear and important distinction for researchers: that unlike a search engine, with static and stored results, ChatGPT never copies, retrieves or looks up information from anywhere. Rather, it generates every word anew. You send it a prompt, and based on its machine-learning training on massive amounts of text, it creates an original answer. Most importantly, each chat retains context during a conversation, meaning that questions asked and answers provided earlier in the conversation will inform responses it generates later. The answers, therefore, are malleable, and the user needs to participate in an iterative process to shape them into something useful.