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Artificial Intelligence (AI), chiefly in the forms of machine learning, natural language processing, and computational topic modeling, is fueling the new generation of e-discovery and contract due diligence tools exploding on the legal market. But AI is also taking hold in my more wonky world of legal academia.
In Topic Modeling the President: Conventional and Computational Methods (or here), recently published in the George Washington Law Review with co-authors John Nay and Jonathan Gilligan, we demonstrate how these tools can tap into large bodies of legal text to help reveal patterns and categories that might not be easily apparent to the human researcher’s eye. The (rather long) article abstract explains our project and the potential for using AI in legal studies:
Law is generally embodied in text, and lawyers have for centuries classified large bodies of legal text into distinct topics—that is, they “topic model” the law. But large bodies of legal documents present challenges for conventional topic modeling methods. The task of gathering, reviewing, coding, sorting, and assessing a body of tens of thousands of legal documents is a daunting proposition. Yet recent advances in computational text analytics, a subset of the field of “artificial intelligence,” are already gaining traction in legal practice settings such as e-discovery by leveraging the speed and capacity of computers to process enormous bodies of documents, and there is good reason to believe legal researchers can take advantage of these new methods as well. Differences between conventional and computational methods, however, suggest that computational text modeling has its own limitations. The two methods used in unison, therefore, could be a powerful research tool for legal scholars.
To explore and critically evaluate that potential, we assembled a large corpus of presidential documents to assess how computational topic modeling compares to conventional methods and evaluate how legal scholars can best make use of the computational methods. We focused on presidential “direct actions,” such as Executive orders, presidential memoranda, proclamations, and other exercises of authority the President can take alone, without congressional concurrence or agency involvement. Presidents have been issuing direct actions throughout the history of the republic, and although the actions have often been the target of criticism and controversy in the past, lately they have become a tinderbox of debate. Hence, although long ignored by political scientists and legal scholars, there has been a surge of interest in the scope, content, and impact of presidential direct actions.
Legal and policy scholars modeling direct actions into substantive topic classifications thus far have not employed computational methods. To compare the results of their conventional modeling methods with the computational method, we generated computational topic models of all direct actions over time periods other scholars have studied using conventional methods, and did the same for a case study of environmental-policy direct actions. Our computational model of all direct actions closely matched one of the two comprehensive empirical models developed using conventional methods. By contrast, our environmental-case-study model differed markedly from the only empirical topic model of environmental-policy direct actions using conventional methods, revealing that the conventional methods model included trivial categories and omitted important alternative topics.
Provided a sufficiently large corpus of documents is used, our findings support the assessment that computational topic modeling can reveal important insights for legal scholars in designing and validating their topic models of legal text. To be sure, computational topic modeling used alone has its limitations, some of which are evident in our models, but when used along with conventional methods, it opens doors towards reaching more confident conclusions about how to conceptualize topics in law. Drawing from these results, we offer several use cases for computational topic modeling in legal research. At the front end, researchers can use the method to generate better and more complete topic-model hypotheses. At the back end, the method can effectively be used, as we did, to validate existing topic models. And at a meta-scale, the method opens windows to test and challenge conventional legal theory. Legal scholars can do all of these without “the machines,” but there is good reason to believe we can do it better with them in the toolkit.
In 1984, William Harrington, then a lawyer practicing in Connecticut, penned an article in the Law Library Journal titled “A Brief History of Computer-Assisted Legal Research.” It provides a fabulous history of the rise of Westlaw and Lexis. At the end of the article he discusses the benefits of there being not one, but two legal research platforms. Looking to the future, he closes the article with this scenario, which must have seemed radical at the time:
Someday before long the computer in your office may be wakened at 2:00 a.m. by a signal from a satellite. Down from the satellite will come a stream of information, which your computer will receive and file in the appropriate electronic cubbyholes in its memory. When you arrive at your office in the morning, your computer will have prepared a daily digest for you of information selected according to instructions you have left with the computer. When you want to do research, you will use your own computer to scan the information in its own memory, information that is updated daily and perhaps even more often.
Almost 35 years later, you can conduct legal search on your phone using any of a dozen or more platforms! We’d all be bummed if we had to settle for Mr. Harrington’s vision. But, how much better can legal search technology get?
I don’t want to commit the same kind of undershoot that Mr. Harrington did, and anyone who knows me knows I think AI is only just beginning to transform the life of lawyers and of the law. Yet, there are some inherent limits on what more AI can squeeze out of legal search.
In the first place, today’s legal search technology actually is pretty awesome. Close to a dozen platforms offer fast, easy, effective legal search options using some or all of machine learning, natural language processing, and computational topic modeling. Some, like CaseText’s CARA, and the more recent Eva by ROSS’s, even dispense with the need to enter a “search” by allowing one to drag and drop a draft brief or memo into a portal that identifies more cases like those already cited. And the visualizations Ravel and FastCase provide allow deeper searching based on citation networks. When I enter a traditional Boolean or natural language search I am impatient if it takes more than three seconds to get high quality results, and then I can resort results based on relevance, date, court, etc. Fastcase also has democratized legal search by teaming up with state and local bar associations that make access free with membership. Bottom line: legal search is already super-fast at producing on target results and available to all. How much faster and on target can it be?
One outer limit is that the dataset is finite. It’s growing, but at any search moment it is finite. So it’s not like one platform can claim to have more federal cases or state statutes than another. In other words, the platforms are not competing based on datasets, they are competing based on how they help us search through the finite dataset.
Another limit is that typically lawyers have fairly specific searches in mind, so most of the dataset is irrelevant to any search. A good search platform will weed out the noise and zero in on cases, statutes, and other materials on target to the specific inquiry. The existing platforms are already quite good at doing both, and doing it fast.
So, what’s left to improve on? Well, it turns out that the same search entered into the various platforms does not yield the same “top ten” cases in terms of relevance. Law professor Susan Nevelow Mart conducted such a test and reported her results and assessments in a thoughtful article published in the March 2018 ABA Journal. It is well worth the read, showing as the bottom line that, much like the different results one might get for the same music genre across Pandora, Spotify, and Apple Music, the different platforms have unique algorithms that push different cases to the top of the list. She also showed they all basically return the same list of cases—it’s the “top” hits that differ starkly. It also turned out, however, that many of the cases in each platform’s “top ten” were actually not relevant to the search once evaluated by a human.
So, there is room for improvement–still more AI can do to improve legal search if the goal is to have that top ten list contain the most relevant cases. Let the games begin!
As I plan and prepare for the Third Annual AI & Law Workshop, scheduled for April 19-20 here at Vanderbilt Law School (details to follow), I thought back to last year’s workshop and my 2×2 matrix of the AI & Law space. I broke it down based on the “AI for Law” and “Law for AI” distinction on one axis and the “Theory and Research” and “Practice and Experience” split on the other. In retrospect, after a year of editing the SSRN Law eJournal on Artificial Intelligence – Law, Policy, and Ethics, I have unpacked it to more fully represent the breadth and depth of the AI & Law world. Here’s my shot at it, with examples of the content and types of questions that fit in each box:
|THE AI and LAW MATRIX||AI for Lawyers
Applications of AI within legal practice
|AI for Legal Administration
Applications of AI in the work of courts and agencies
|Law for AI
Legal regimes governing the use and impacts of AI
|AI for Law for AI
Employing AI to implement Law for AI regimes
How do we conceptualize AI in this space?
|When is AI “practicing law”?||Can AI “judge”?
Can AI design standards better than agencies?
|Does machine learning “discriminate” within the meaning constitutional and civil rights laws?||Can AI make AI obey the law?|
What are the moral implications and ethical duties?
|What duties do lawyers have when incorporating (or not) AI in practice?||Is it ethically sound to turn over decisions such as bail and agency enforcement to AI?||How transparent should government be when it uses AI to monitor?||Is it morally acceptable to turn regulation of AI over to AI|
What are the societal goals and tradeoffs?
|What level of AI knowledge should lawyers be required to have?||Do we want to promote or contain AI in criminal law administration—e.g., setting bail?||What concerns are there regarding using AI to develop “threat scores” and “citizen scores”?||Who decides what AI applications to regulate with other AI?|
How do we design legal instruments and institutions?
|Who is liable for AI’s role in malpractice?||How will machine learning be admitted as evidence in the court room—under Daubert or a new standard?||Is a government produced “citizen score” an invasion of privacy? A violation of due process? Of equal protection?||How could we design mandatory AI monitoring and reporting of use of AI in private employment decisions?|
What are the practical implications?
|How will lawyers actually use and evaluate others’ use of AI?||How will we deal with different levels of access to AI by parties?||Do agencies have the capacity to design and administer regulatory AI?||How will AI be deployed on top of AI as a technical matter?|
What is the track record?
|How far have smart contracts gained traction in commercial transactions?||Is there evidence that use of AI in probation is more or less discriminatory than human judges acting alone?||Have anti-discrimination laws been effective in regulating uses of AI in housing, lending, and other private decisions||Is there evidence from AI development that the “black box” of machine learning can be “interrogated”?|
I plan to float this at the workshop and again at our Summit on Law & Innovation, which my colleagues Larry Bridgesmith and Cat Moon are organizing for April 30, and I also welcome comments.
Along with my three wonderful co-authors—Eric Biber of UC-Berkeley Law, Sarah Light of Penn’s Wharton School Legal Studies Department, and Jim Salzman of UCLA Law—I was pleased recently to have published Regulating Business Innovation as Policy Disruption in the Vanderbilt Law Review. As its title suggests, the article explores instances of “disruptive business innovation” to sort out why some (e.g., Uber and Airbnb) explode into policy disruptions and others (e.g., Netflix and Swiffer) do not. After all, both Uber and Netflix upended incumbent industries, so why is Uber facing legal snarls all over the globe and Netflix destroyed Blockbuster with nary a legal scratch?
Reading about the lawsuit Legal Force recently filed against Legal Zoom (and the California, Arizona, and Texas bars) in California, claiming it is engaged in unauthorized practice of law in its online trademark documents service, I was stuck by an irony about our article: with four law professors as co-authors, we included not a single example from the legal industry! Legal Zoom is the obvious example we could have used, but the problem runs deeper and threatens the access to justice movement.
But first, some background on what we mean by policy disruption. Over in the business school world, there is a raging debate over the merits of Clayton Christensen’s theory of disruptive business innovation. The basic idea is that by using a technological or business model innovation (or both), an innovator can quietly eat away at the “low end” of an incumbent industry’s customer base—the customers who would pay less to get less but don’t have that option under the incumbent industry’s model. Over time, though, the innovator improves its product quality and penetrates deeper into the market before the incumbents wake up, by which time it is too late. Legal Zoom is a great example: it started out doing limited, routine legal tasks and now does a lot more, including feeding work to a network of lawyers, with annual revenue upward of $200 million.
In the article, we do not try to resolve the debate over whether the business theory of innovative business disruption is useful or not—that’s for the business profs to decide. Our point is that, as far as we can tell, the impact of regulation response never plays a role in that debate, but regulation may have everything to do with whether a business innovation succeeds or not. So we developed a theory for thinking about when business disruption raises a policy disruption under the existing regulatory regime applied to the incumbents–a disconnect that could attract regulators’ attention. There are four types of business innovation policy disruptions:
- End Runs – the innovator argues it is sufficiently different from the incumbents to avoid being subject to the incumbent regulatory regime
- Exemptions – the innovator argues it fits an exemption in the incumbent regulatory regime
- Gaps – the innovator is engaging in an activity that fits no existing regulatory regime but presents policy concerns like those that led to the incumbent regulatory regime
- Solutions – the innovator is subject to the existing regime, but if left alone would help solve the problem that led to the regulation of the incumbents in the first place
Legal Zoom is a clear example of an End Run—the company argues it is not engaging in unauthorized practice of law but comes about as close to the line as one can imagine, which has ruffled the feathers of lawyers and state bars around the nation since the company started in 2001. The Legal Force lawsuit claims Legal Zoom has crossed the line and is unfairly cutting into Legal Force’s business as a law firm specializing in trademark practice. We would identify this as a clear policy disruption problem—the incumbent (Legal Force) argues that the innovator (Legal Zoom) presents the same policy concerns that led to the regulation of the incumbent and thus should be subject to the same regulatory regime in order to avoid giving it an unfair market advantage, but Legal Zoom argues it is not practicing law so is not subject to the regulations.
We argue in the article that in such situations regulators have four choices:
- Block – prohibit the innovator model altogether
- OldReg – apply the incumbent regulatory regime as is and see how it fares
- NewReg – invent new regulations for the innovator model (and possibly the incumbents)
- Free Pass – leave the innovator alone and let the market chips fall where they may
As the ABA Journal has covered extensively, so far the Legal Zoom battle in the US has for the most part been between advocates of OldReg versus advocates of Free Pass, although some states, such as North Carolina, have adopted NewReg approaches. By contrast, in the UK, their embrace of a NewReg approach to legal practice has allowed Legal Zoom far more latitude.
As I argue in a forthcoming installment of my Post Normal Times column in ABA’s The Young Lawyer magazine, I don’t see much future for the limiting this ongoing debate to the OldReg vs. Free Pass options. Neither do the DOJ and FTC, which argued in support of the North Carolina reform. There is mounting pressure to harness advanced technologies through innovative business models as a way to improve access to legal solutions for low- and middle-income individuals and small businesses, who simply cannot afford traditional legal services delivery. Other legal innovation upstarts, like the ticket resolution app TIKD, are getting stymied by the relentless battle with OldReg forces. As the DOJ and FTC argued:
“the practice of law” should mean activities for which specialized legal knowledge and training is demonstrably necessary to protect consumers and an attorney-client relationship is present. Overbroad scope-of-practice and unauthorized-practice-of-law policies can restrict competition between licensed attorneys and non-attorney providers of legal services, increasing the prices consumers must pay for legal services, and reducing consumers’ choices. … Such products may also help increase access to legal services by providing consumers additional options for addressing their legal situations. The Agencies also recognize that such interactive software products may raise legitimate consumer protection issues. The Agencies recommend that any consumer protections, such as requiring disclosures, be narrowly tailored to avoid unnecessarily inhibiting competition and new ways of delivering legal services that may benefit consumers.
Put bluntly, if the legal profession is serious about improving access to justice, we are going to have to get serious about designing a NewReg model that both allows legal industry innovations to thrive and protects consumers and the public.
The fifth year of my Law Practice 2050 class is a wrap and it was wonderful working with the students and guest speakers. I’ll give a shout-out to the speakers soon—for now I want to highlight the tremendously creative topics my students bit off for their “skate to where the law is going” project. The project requires them to build a future scenario around an emerging technological, social, economic, or other trend, anticipate the legal issues it will generate, and then explore the theme in three different writing projects—a blog post, a client alert letter, and a bar journal article. The idea is that when the show up at their first post, they need to do more than show up—they need to brand and build their expertise. What better way to do so than on an issue for which there are no existing experts!
It has amazed me how quickly topics my students chose five years ago have ramped up into real legal practice fields (think cryptocurrencies, 3D printing, drones, and fitness tracker data, all of which were just breaking five years ago), and how much even those have changed and generated new applications and thus new legal angles. So, if you are looking for where billable hours will emerge over the next five years, look at this year’s project topics:
- Quantum computing
- Brain-to-computer and brain-to-brain neural links
- Microchip implants for employees
- Automated shipping vessels
- Cyborg enhancements
- Cryptocurrencies for small business
- Initial coin offerings
- CRISPER gene editing
- Smart contract oracles
- Preimplantation genetic diagnosis
- Synthetic food
- Lab-grown in vitro meat
- Augmented reality
- Virtual reality
- 3D food printing
- Autonomous aerial vehicles
- Germline editing
- Life-extending nanotechnology
- Twitter bots
- Implanted medical drug release chips
- Cannabis law
- Data driven threat scores
- AI displacement of jobs
- Voice activated digital assistants
- MOF water capture technologies
- Implanted video recording devices
- High-tech deep sea mineral extraction
- Opening of Arctic shipping lanes
- Stimulus and biomarker detection devices
- Fitness tracker employee data
- Service animals and the ADA
- Mega-scale ecological engineering
Several topics were more directly related to legal practice:
- Emojis in the courtroom
- Alternative legal finance
- Brain scans as evidence of state of mind
- Unauthorized practice of law liberalization
Some of these topics already are generating legal work and legal practice challenges, but not at large scales; others have yet to translate to the legal space, but that is soon to come; some seem too outlandish to ever generate billable hours or legal practice concerns, but they will.
And one thing is for sure—reading these final bar journal articles will beat grading exams!
The slow pace of my posts on Law 2050 lately has a lot to do with the fast pace of Skopos Labs, the legal-tech start-up I mentioned in my last post (Yikes–that was in June!). I am happy to report that after a busy summer, with John Nay leading an excellent data analytics team, our first product will be included as part of the newly-launched Wolters Kluwer service–the Federal Developments Knowledge Center. Read all about it here: http://wolterskluwer.com/company/newsroom/news/2017/09/wolters-kluwer-introduces-ai-powered-predictive-analytics-to-federal-developments-knowledge-center.html.
The quick version:
Collaboration with Skopos Labs, Inc. will enable practitioners to predict the likelihood of bills becoming law
NEW YORK, Sept. 14, 2017 — Wolters Kluwer Legal & Regulatory U.S. announced today the introduction of a powerful new predictive analytics package as an augmentation to its highly regarded Federal Developments Knowledge Center. The analytics are powered by artificial intelligence (AI) tools developed in collaboration with Skopos Labs Inc., a software company specializing in predictive analytics. The new features are the latest in what has been a continual stream of innovation and harnessing of analytics and AI across several Wolters Kluwer product lines.
Working on this has been fun, but it has sucked up a lot of my other fun time, such as posting here. Seeing some daylight with this recent Skopos development announced, I plan to get back at it. In particular, my Law 2050 class is well underway and this year’s slate of guest speakers is tremendous–I am thankful to them all. More to follow!
One of the high points of each year in our Program on Law & Innovation is the “pitch event” in Adjunct Professor Marc Jenkins’ Technology in Legal Practice class. One of the major projects in the class involves students forming teams that pair with area legal aid organizations to build problem-solving apps improving access to justice. Now wrapping up its third year, the class and the students are firing on all pistons, building prototypes or live versions of some very meaningful apps that can help traditionally underserved populations who cannot affordably navigate our utterly complex legal system. Marc has worked closely with the legal aid organizations to develop strong bonds with the students, and also has opened ties with Vanderbilt’s Computer Science Department and our new entrepreneurship center, The Wond’ry, to leverage their expertise in building out the apps. Here’s just a quick summary of the students’ impressive accomplishment this year, describing for each team the organization, work product, and app authoring platform:
- LGBT Legal Relief Fund: This new organization has been flooded with requests for help. The student team worked with the developers at KIM to build a workflow management app.
- Legal Aid Society: The team built a mobile app prototype, which they named Clean Slate, to guide a person through the incredibly complicated criminal record expungement eligibility process. They used the JustinMind Mobile App prototyping tool.
- Tennessee Justice for Our Neighbors: Using an app authoring platform designed by Vanderbilt CS undergrad student Ashley Peck (very impressive!), this team developed a prototype of what they call the Childcare Contingency Plan for undocumented immigrants hoping to contingency plan for their children in case the parents are detained or deported.
- Tennessee Justice Center: This student team designed an app for the Sales Force platform that walks families through the SNAP (food stamps) eligibility criteria. They reduced 1000 pages of ridiculously complicated agency “guidance” to an interview consisting of 30 – 60 questions (depending on answers).
- Nashville Arts and Business Council: This team picked up from a previous year’s team that used Neota Logic to design an interview aspiring musicians (we have a few here in Nashville!) can use to make business entity formation decisions appropriate to their plans. The team essentially beta tested the existing app, leading to improved wording and more accurate outcomes.
- Legal Aid Society: This team also continued working on a mobile website app started by a prior team, built using the same authoring program designed by Ashley Peck, to guide the user through the often bewildering debt collection process.
- Legal Aid Society: Using the A2J author platform, this team designed a web-based computer app they call Mission Expungement, for the criminal records expungement process directed specifically at the Nashville jurisdiction.
I am pleased to announce that the Program on Law & Innovation at Vanderbilt Law School is the sponsor of the new SSRN eJournal, Artificial Intelligence – Law, Policy & Ethics. The journal publishes abstracts and papers focused on two themes: “AI for Law,” covering the increasing application of AI technologies in legal practice, and “Law for AI,” covering the issues that will arise as AI is increasingly deployed throughout society. I am serving as the editor, supported by a wonderful Advisory Board.
If you are working on a paper in this domain, please consider including our journal when posting to SSRN, and if you have an SSRN subscription, please consider adding our journal to your feed.
Last week Vanderbilt’s Program on Law & Innovation held our Second Annual Workshop on Artificial Intelligence and Law, and it was a truly wide-ranging and inspirational set of presentations and roundtable discussions.
One way I think about this topic is to (artificially) unpack it into four themes, as shown in this 2×2 space:
AI for Law
Law for AI
Research and Theory
Practice and Application
The idea is that AI will both be deployed in legal practice and, as it is deployed in society generally, will raise ethical and policy concerns requiring legal responses. In both of those realms, work is needed on the theory and research side to facilitate and manage how AI is applied in practice.
Our workshop presentations and discussions covered all the boxes, and many demonstrated that the boxes are not hermetically sealed—some themes and questions are cross-cutting. Indeed, several participants have engaged in a lively post-workshop email discussion on the extent to which using AI in dispute resolution could lock in doctrine or could be “programmed” for creativity, a question that requires engaging both theory and practice.
Even if one is skeptical about how soon we will see “general AI” coming online, if ever, there’s no question that “weak AI” is getting stronger and stronger in both the AI for Law and Law for AI realms. There’s no way to navigate around it! We engaged it in the workshop starting Thursday with big picture overviews of the two overarching themes by Oliver Goodenough (AI for Law) and John McGinnis (Law for AI). Friday had both deep dives and high-level theory in play. For example, Michael Bess asked how we should act now to avoid pitfalls of ever-stronger AI. Dan Katz discussed his work on predicting legal outcomes with AI tools combined with expert and crowd predictions. Jeannette Eikes outlined an agenda for building AI-based contract regimes. John Nay used topic modeling to parse out features of Presidential exercise of power that would have taken years to accomplish using traditional research methods. Cat Moon and Marc Jenkins unpacked AI in the legal practice world, showing where it faces uptake bottlenecks, and Doug Fisher kicked off a discussion of what AI means in the AI research world. Jeff Ward offered an insightful examination of the challenges AI will present for Community Economic Development programs, as well as the uses CEDs can make of AI. In short, we covered a lot of the boxes, and more!
Many thanks to this year’s participants—I’m looking forward to planning next year’s gathering as well!
Long Time No Post! I’ll explain why later. For now, I’m diving back into Law 2050. First up in the post order is news about this week’s workshop on AI & Law. Here’s the scoop about this great lineup of participants and themes we’ll cover:
Second Annual Workshop on Artificial Intelligence and Law
Vanderbilt University Law School
Program on Law & Innovation
March 2-3, 2017
The Workshop on Artificial Intelligence and Law each year brings together academics and practitioners working in one or both of two themes—AI for Law, which explores how AI will be deployed in legal research and practice; and Law for AI, focused on the legal, policy, and ethical issues that the deployment of AI in society is likely to create. This year’s workshop includes some of the nation’s most thoughtful experts and thinkers in both spaces. Thursday afternoon sets the scene with two presentations tapping into the two big themes to help frame a “big questions” discussion. Friday’s agenda intersperses research and practice presentations representing both themes, circling the agenda back to the “big questions” question—did we answer any, or at least chart the next steps?
Thursday, March 2
Burch Room (1st Floor)
3:00 – 3:30 Welcome and Introductions
3:30 – 4:00 Oliver Goodenough, Vermont Law School: Law as AI
4:00 – 4:30 John McGinnis, Northwestern University Law School: Discussion Lead – Breakaway AI
4:30 – 5:00 Roundtable: What are the big questions?
5:00 – 6:30 Free Time
6:30 Dinner at Amerigo, 1920 West End
Later on? Broadway music venues
Friday, March 3
Bass Berry Sims Room (2nd Floor)
8:00 – 8:30 Breakfast in meeting room
8:30 – 8:45 Additional Introductions
8:45 – 9:15 Dan Katz, IIT Chicago-Kent Law School: Predicting and Measuring Law
9:15 – 10:15 Cat Moon, Legal Alignment, and Marc Jenkins, Asurion: Discussion Leads – AI in Practice
10:15 – 10:30 Break
10:30 – 11:00 Michael Bess, Vanderbilt University History Department: Human-level AI and the Danger of an Intelligence Explosion: Questions of Safety, Security, and International Governance
11:00 – 11:30 Jeff Ward, Duke University Law School: A Community Economic Development Law Agenda for the Robotic Economy
11:30 – 12:00 Doug Fisher, Vanderbilt University Computer Science: Discussion Lead – Unpacking AI
12:00 – 1:00 Lunch and conversation in meeting room
1:00 – 1:30 John Nay, Vanderbilt University College of Engineering: Analyzing the President—the First 100 Days
1:30 – 2:00 Jeannette Eikes, Vermont Law School: AI for Contracts
2:00 – 2:30 J.B. Ruhl, Vanderbilt University Law School: Envisioning and Building “Legal Maps”
2:30 – 2:45 Break
2:45 – 3:15 Roundtable: Did we answer any of the big questions?
3:15 – 3:30 Closing remarks and next steps