Today marks the last session of Year 6 of my Law 2050 class at Vanderbilt Law School, which surveys the transformative forces at play in the legal industry and serves as the capstone course for the Program on Law & Innovation‘s core curriculum.
This year 24 marvelous guest speakers contributed their experiences and insights to the class. My shout-out to them (in order of appearance):
- Zach Fardon, King & Spalding
- Joan Fife, Winston & Strawn
- Jeff Grantham, Maynard Cooper
- Anna Barry, Jounce Therapeutics
- Michelle Kennedy, Nashville Predators
- Craig Weinstock, National Oilwell Varco
- Daniel Reed, CEO of United Lex
- Larry Bridgesmith, Adjunct Professor and PoLI Coordinator
- Caitlin Moon, Adjunct Professor and PoLI Innovation Design Director
- John Murdock, Bradley Arant
- Professor Nancy Hyer, Vanderbilt’s Owen School of Management
- Jessica Gilchner, Senior Director of Pricing and LPM Solutions, Pillsbury
- Randy Michels & Kevin Hartley, Trust Tree
- Ray LaDrier, Locke Lord
- George Lamb, Baker Botts
- John Lutz, Vanderbilt Vice Chancellor for IT
- Patrick Cavanaugh, Blank Rome
- Kito K. Huggins, Director, Executive Administration, Weil, Gotshal & Manges
- Walt Burton, Thompson Burton
- James Mackler, Mackler Law Firm and Of Counsel to LeClaire Ryan
- Andy Bayman and Mike Duffy, King & Spalding
- Justin Ergler, GlaxcoSmithKlein
Many thanks to you all!
To learn more about the class and the themes each speaker addressed, see the full syllabus here.
I’m looking forward to Year 7!
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.
By Micah Bradley
Do you love waking up to the smell of sizzling bacon? In 2014, Oscar Mayer held a sweepstakes for a device that could plug into an iPhone to emit the aroma of bacon as a morning alarm rang. Oscar Mayer received almost 150,000 applications for the few thousand diffusers, and the company even won “Most Creative Use of Technology” at the Shorty Awards for Social Media.
Though previous scent technologies had limited success, growing interest in aromatherapy products and in scent advertising for brick-and-mortar stores will likely lead to scent diffusion devices for smart phones, or even technological integration into phones themselves. These scents might be triggered by a user through apps for relaxation or by companies through scented advertisements or shopping websites. Some current ventures include oNotes, which connects to phones via Bluetooth and has Spotify-style scent playlists, and Scentee, which sells cartridges that emit scents from phones.
The rise of scent technology begs the question—can you trademark a scent? Though it is possible, reportedly only about ten scents had been trademarked as of three years ago. However, brands have shown an increasing interest in trademarking scents. For example, Verizon recently protected its stores’ “flowery musk scent.”
Trademarking scents is difficult. The scent must be both “nonfunctional and distinctive.” Ironically, in order to be considered nonfunctional, if the product’s only purpose is smell related (such as a perfume), instead of helping to distinguish a brand, it is not trademarkable. In addition, there can be difficulties in applying for the trademark, such as providing samples of the scent to a government examiner. As of now, Verizon would be able to puff out its protected “flowery musk scent” while other brands have no protection for scents they want consumers to associate with their brands.
Besides intellectual property, two other issues that may come with scent technology are tort and criminal claims. Texting obnoxious smells like farts could result in nuisance claims. Phones could also emit smoke or chemical smells, resulting in criminal or negligence charges.
These technologies are still emerging, and it may be several years before we see their full incorporation into phones or other devices. Clients should stay ahead of the curve, as Verizon has, and trademark their signature scents now.
By Emily Lamm
Cryptically crafted and living behind the façade of technology, algorithms have escaped the standards we hold ourselves to. The allure of coding and quantum computing arouses a sense of intrigue and elevates the status of the underlying algorithms. Yet, this charm should not obscure the fact that the authority afforded to technology is constructed and highly sensitive to context. For instance, when a deep learning, neural network is introduced to an incongruous object––an elephant within a living room––pixels are crossed and previously detected objects are misidentified. These types of errors are not uncommon, but they do take on forms far more sinister than an elephant-triggered kerfuffle. High-profile examples include LinkedIn’s platform showing high-paying job ads to men more frequently than women, and law enforcement officials and judges relying upon patently racist AI-powered tools.
On one hand, the United States has developed a robust body of laws combating discrimination. The Equal Protection Clause of the Fourteenth Amendment and Title VII of the Civil Rights Act have been paramount, and the Americans with Disabilities Act of 1990 is considered an immense success in protecting individuals with qualifying disabilities. On the other hand, the United States has no such analogue to offer protection from algorithmic bias. In effect, algorithms––just one step removed from humans––have escaped the rule of law despite being a reflection (or manifestation) of the implicit values of the very humans who created them.
Now, just because there is no general legislation or regulatory scheme to control for algorithmic bias, doesn’t mean there won’t be soon. Other countries have filled this gap by implementing a data protection regime. In due time, perhaps with a change of administration, we will begin to see a drastically different approach to Artificial Intelligence. Although Americans have been rather lackadaisical about data privacy (often trading their Facebook information for a quiz predicting what their child will look like), they have been quick to advocate against discrimination. Just look to the sweeping nature of the civil, women’s, and LGBT rights movements. Accordingly, there are numerous initiatives––launched by the likes of Facebook, IBM, Google, and Amazon––researching algorithmic bias and announcing tools to bolster AI fairness.
Lawyers are also not immune from the mysterious nature of algorithms. Indeed, most litigators interface with it regularly. Every time we run a search in Lexis Advance or Westlaw, the results we see are the product of algorithms hard at work behind the scenes. Recently, Fastcase provided the option for users to toggle with its research algorithms through factors like relevancy and authoritativeness. Although this tool appears to have little influence upon the results generated, it is responsive to a growing demand for algorithmic accountability. Undoubtedly, lawyers today must embrace and implement technology in order to remain at the forefront of the industry. Nevertheless, lawyers must also continue to be skeptical, discerning, and autonomous thinkers that refuse to grow complacent with inadequate technology.
As the United States citizenry grows increasingly diverse, technology’s “black box” must begin to encompass an intersectional awareness that accounts for the vast array of identities its users embody. Ensuring that technology is implemented and monitored responsibly should be at the forefront of everyone’s mind. Whether it be lobbying for new legislation or updating corporate policies, the time is ripe to seriously consider the role of law in algorithmic bias.
Each year in my Law 2050 class at Vanderbilt Law School, students identify an emerging technological, economic, environmental, or social trend and project it into the future to explore how it might generate law and policy issues needing lawyers’ attention. They write a blog post about it, then a client alert memo, then a bar journal article. They can choose any practice perspective defining who they and their clients are: private practice, government, plaintiffs, public interest, international, etc. The goal is to instill curiosity, entrepreneurship, and writing skills to put them “on the map” as they start out in practice. (For a great example of this exercise in scenario building for lawyers, check out Carolyn Elefant’s excellent ebook: 41 Legal Practice Areas that Didn’t Exist 15 Years Ago.)
I’ve been doing this for six years, and it has amazed me how many new themes come into the picture each year that weren’t on the radar screen the year before. Even the themes that have come up before have evolved so rapidly that they present entirely new dimensions to explore.
Below are this year’s themes—what an impressive list! I’ll bet you haven’t even heard of some of them. I’m really looking forward to reading my student’s bar journal articles to see where they take these:
- Electric scooters
- Malicious audio/video editing
- Social credit system & facial recognition
- Predictive policing
- Advanced energy storage for wind & solar
- Private space exploration
- Dark web policing
- Social media influencers
- Genealogy technology & policing
- Emerging technology trade controls
- Space trash
- Algorithm bias
- Health insurer role in opioid crisis
- Cannabis law legal conflicts
- Augmented reality
- In vitro fertilization parental tech and parental rights
- Scent technology
- Implantable microchips
- Alexa and criminal enforcement
- Private artificial islands
- Radio frequency electric charging
- Geoengineering—solar radiation management & CO2 removal
- Non-bank fintech
- Biometric privacy
- E-sports industry
- Medical record blockchain
- Cultured meat regulation
- Emotional AI
- NCAA rules for high school pro drafts
- Initial coin offerings
- Data privacy regulation (GDPR)
- Smart microgrids
- Law firm insourcing of non-legal services
- Freebooting video content
- Artificial embryos
- 23&Me health testing
- Voice cloning issues
- Arctic circle transportation and minerals
- Alternative legal services providers and legal malpractice
- Blockchain and real estate titles
- Sport betting and machine learning
- Personal data sales and privacy regulation
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!
Resilience theory has become a dominant framework across many disciplines, from engineering to ecology. Resilience is formally deﬁned as “The capacity of a system to absorb disturbance and reorganize while undergoing change so as to still retain essentially the same function, structure and feedbacks, and therefore identity, that is, the capacity to change in order to maintain the same identity” (Folke et al. 2010). In the theoretical model, “engineering” resilience refers to building in hard barriers to disturbances, such as a concrete seawall to fight off big storms, whereas “ecological” resilience refers to methods that bend more but bounce back, such as enhancing coastal wetlands to take the brunt of the storm.
Ten years after the Great Recession swept through the economy like a big storm, we can ask, how resilient was the legal services industry and how resilient is it today? This gets us deeper into what goes into resilience. There are five attributes, with some trade-offs at play:
- Reliability: The parts of the system have to perform as expected, and the system has to perform if a part fails
- Efficiency: The system should minimize waste and perform as expected even in times of resource scarcity
- Scalability: The system can perform as expected even as its scale increases or decreases
- Modularity: The system can rearrange and replace its parts to respond to disturbance
- Evolvability: The system can make changes necessary to perform as expected over long time frames
Engineering resilience is often associated with boosting reliability and efficiency, whereas ecological resilience is often more about working on scalability, modularity, and evolvability. You can quickly see where some of the trade-offs could complicate matters. For example, to build scalable and modular features in a system may require redundancy of parts, which may not always promote efficiency. Optimal efficiency would build in just the right amount of redundancy to keep the system resilient, but knowing how much that is can be a challenge.
Looking back on it, I’d say the legal services industry was pretty resilient to the Great Recession. So-called Big Law is back on the rise when measured by revenues and profits, albeit still less so than before the recession. And the emergence of significant new forms of legal services providers, such as United Lex and Integreon, and an array of new technology solutions suggests that the legal services industry is building modularity and scalability in order to evolve. And there are other positive signs, such as increasing employment and increasing law school applicants. Bottom line: contrary to all the “death of lawyers” rhetoric at the beginning of this decade, it didn’t happen—the industry was resilient. Yes, it has changed, but change to some degree is a hallmark of evolvability, an essential ingredient of resilience. The question is whether it has maintained the same identity, and I would say for them most part, it has.
But how resilient is it still? What if another recession even half as bad as 2008 hit the economy in two years? The concern may be that the legal services industry, and Big Law in particular, has been so driven by the efficiency goal that it has dispensed with too much redundancy to take another head on blow like that. A concrete seawall may provide more immediate protection than a coastal wetland, but when it blows out, it’s ugly. In short, keep an eye on continuing to build scalability, modularity, and evolvability too.
When we started the Program on Law & Innovation here at Vanderbilt Law School five years ago, we launched with two courses: Legal Project Management, taught by PoLI Coordinator and Adjunct Professor Larry Bridgesmith, and my Law 2050 class that surveys the post-normal times in the legal industry. With a strong commitment to delivering vital curricular content to our students, I am happy to report that we have now built out to ten courses, five of which are in the PoLI “core” course set plus five more advanced and specialized courses firmly within the PoLI space:
- Law 2050
- Legal Project Management
- Legal Problem Solving (taught by Cat Moon, our new Director of Innovation)
- Law as a Business
- Legal Practice Technology
- Blockchain and Smart Contracts
- Robots, AI, and Law
- Role of In-House Counsel
- Disruptive Technologies and Law
- Corporate Legal Risk Management
With this diverse and deep set of course offerings, we aim for PoLI to equip our graduates to dive into the “river” of change in the legal industry and see it as an opportunity, not a threat.
As I put the finishing touches on my Law 2050 class syllabus for this fall semester, I am struck by two impressions. The first is the tremendous generosity my guest speakers have shown in the past, devoting their time and energy to providing perspective and insight to the students, and this year is no exception. So far the following have agreed to donate their time, roughly in order of appearance:
- Zach Fardon – King & Spalding
- Joan Fife – Winston & Strawn
- Jeff Grantham – Maynard Cooper
- Anna Barry – Jounce Therapeutics
- Michelle Kennedy – Nashville Predators
- Craig Weinstock – National Oilwell Varco
- Larry Bridgesmith – Adjunct Professor and PoLI Coordinator
- Caitlin Moon – Adjunct Professor and PoLI Innovation Design Director
- John Murdock – Bradley Arant
- Nancy Hyer – Vanderbilt Owen School
- Randy Michels & Kevin Hartley, Trust Tree
- Ray LaDrier – Locke Lord
- George Lamb – Baker Botts
- John Lutz – Vanderbilt Vice Chancellor for IT
- Patrick Cavanaugh – Blank Rome
- Kito K. Huggins – Weil, Gotshal & Manges
- Daniel Reed – United Lex
- Diedre Gray – Post Holdings
The second impression is how the framing of the course has changed. When I started the course in 2013, the theme was very much about how much was changing in the legal industry compared to pre-2008. The average 3L student in the 2013 class was 26, meaning they were 21 in 2008 and lived in very real time as young adults through the Great Recession. They experienced the before and after contrast very closely, and, while not doing so as lawyers, easily connected with that theme in the class. The metaphor I used was that pre-2008, the legal industry was like a lake, whereas post-2008 it was more like a river and nobody knew where it was leading. The river was scary.
With each year since then, however, the reset button effect of the Great Recession has become more remote to the students. Yes, they are entering the profession in the midst of change just as were the 2013 students, but they don’t generally use pre-2008 as a reference point for anything, much less for their conception of what the legal industry is about. In short, they don’t care about what the legal industry lake looked like pre-2008—they want to jump into the river! I see the course as more about giving them a raft to navigate it.
The same has been true of my guest speakers, who in 2013 were very much tuned into the shock to the system the Great Recession caused and still reeling from it. They remembered swimming in the calm lake. With each year, the mood has been less “what just happened, take me back to the lake” to more of a focus on change management and seeing opportunities as they raft down the river.
Of course, it’s still the case that nobody knows where the river is leading!
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.