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The Artificial (Intelligence) Restatement of the Law?
As I write, the 2013 International Conference on Artificial Intelligence and the Law is taking place in Rome. I wish I had been able to attend–anyone remotely interested in the scope of Law 2050 should take a look at the program.
Most of the discourse on AI and the Law in the popular press has focused on the capacity AI to predict the law, as with Lex Machina and Lexis’s MedMal Navigator. But if you take a close look at the ICAIL program, the sleeper may be the capacity of AI to make the law. Many of the presentations delve into methods of using algorithms to extract and organize legal principles from the vast databases or cases, statutes, and other legal sources now available. The capacity to produce robust, finely-grained, broad scope statements of what the law is powerful not only for descriptive purposes, but as a force in shaping the law as well.
Consider the American Law Institute’s long-standing Restatement of the Law project. As ALI explains,”the founding Committee had recommended that the first undertaking of the Institute should address uncertainty in the law through a restatement of basic legal subjects that would tell judges and lawyers what the law was. The formulation of such a restatement thus became ALI’s first endeavor.” As I think any lawyer would agree, the idea worked pretty well, pretty well indeed. The Restatements have been so influential that they go well beyond describing the law–they contribute to making the law through the effect they have on lawyers arguing cases and judges reaching decisions.
How did ALI pull that off? Numbers. Anyone who has worked on a Restatement revision committee has experienced the incredible data collection and analytical powers that ALI assembles by gathering large numbers of domain experts and tasking them with distilling the law of a field into its core elements and extended nuances. The process, however, is protracted, costly, tedious, and often contentious.
Many of the ICAIL programs suggest the capacity of AI to generate the same kind of work product as ALI’s Restatements, but faster, cheaper, and perhaps better. ALI depends on large committees of experts to gather case law, analyze it, and extract and organize the underlying doctrines and principles. That’s exactly what AI for law does, only with a lot fewer people, a lot more data, and amazingly efficient and effective algorithms. Of course, you still (for now) need people to manage the data and develop the algorithms, but once you have it all in place you just hit the run button. When you want an update, you just hit the run button again. When you want to ask a question in a slightly different way, just enter it and hit the run button.
As the Restatements demonstrated, a reliable, robust source of reference for what the law is can be so influential as to become a part of the making of the law. As AI applications build the capacity to replicate that work product, it follows that they could have the same kind of influence.
One feature AI could not produce, of course, is the commentary and policy pushing one finds in the Restatements. The subjective dimension of the Restatements has its own pros and cons. The potential of AI to produce highly-accurate, real-time descriptions of the law, however, might change the way in which we approach normative judgments about the law as well.
Reflections on the Good Old Days of Legal (Non)Technology
I showed up for work my first day at my law firm–then (and still) one of the largest in the world–in September 1982. I was assigned to a nice 4th floor window office overlooking Connecticut Avenue in D.C., which gave me a great view of the daily protest parades. My technology consisted of: a phone (land line–there was no other kind), a dictating machine, a wall switch to turn the lights on, and some electric sockets to plug in my desk lamp. That was it. My secretary (the term in use then) had all that plus an IBM Selectric typewriter. Virtually all research was done in the library using books. Somewhere in the library there was a dial-up Westlaw terminal and printer. Wow, we had it all.
Somehow, we managed to practice law.
A few years later we got some newfangled thing called “mag cards,” which allowed our assistants to revise documents by loading a huge stack of floppies into a slot in their IBM typewriters. Soon after that came the first computers. Our firm adopted a Wang system (Wang was one of the leading computer companies in the 1980s, then went bankrupt in 1992) with some kind of intranet e-mail network. Only staff had them–no one could imagine why the attorneys would want or need one.
Somehow, we managed to practice law.
But I wanted one of those things. (If you haven’t caught on by now, I am a tech junkie.) I had moved to the Austin office by then and was put in charge of the summer associate program (a/k/a/ party coordinator–how things change!), so I concocted a total BS story about how I needed a computer at my desk to help me do that. The firm bought it and soon after I had mine, my peers wanted one. Then I bought an IBM PS/50 for home and figured out how to hook into the firm intranet. I discovered telecommuting! One day I was exchanging e-mails with a colleague about a litigation matter and he said he would rather come down to my office to chat about it. I waited. Then the e-mail came: “Where the *&%$ are you?” Wow, were we ever wired up!
Somehow, we managed to practice law.
Envisioning the Law of Expert Robots
Two Canadian philosophers, Jason Miller and Ian Kerr, have posted an article on The Prospect of Expert Robots in which they consider a philosophical question that will have thorny implications for law: What if an expert human and an expert robot disagree on a matter of importance? By expert robot they mean a Big Data-loaded computer juiced up with algorithms that scour the data to produce answers within a complex decision domain that are on average more accurate than the answers counterpart human experts provide. Watson, in other words, is an expert computer at the game of Jeopardy, because it beat the world’s two most expert humans quite handily. But Watson is a toddler compared to the kind of expert computers on our horizon. Google’s driverless car, for example, is operating in a far more complex decision domain than is Watson, and seems to be doing quite a good job of avoiding accidents and traffic violations.
So consider some scenarios in the not too distant future in which expert computers are common throughout a wide array of decision domains and generally outperform their human expert counterparts. In one scenario they have replaced most of the human experts, making decisions free of human oversight. We’ve taken our hands off the wheel, so to speak, and delegated decision making to the expert computers. The expert computers aren’t perfect, however, so they will make mistakes. There will be driverless car crashes. Who’s liable when that happens? Can expert computers be negligent, or act with intent?
The more complex question Miller & Kerr treat, however, is what happens when the expert computers are working alongside human experts to produce good decision results and the two disagree about a crucial decision. Do we go with the human or the computer? If we go with the human and it turns out the computer was right, and the cost of the human’s error is significant, where does liability fall? And the reverse scenario presents the same question.
Miller & Kerr set up these scenarios nicely and work through some of the more profound normative questions they pose, concluding that there will be strong arguments in favor of delegation to expert computers but that the human impulse to retain control might make it difficult for society to take full advantage of what expert computers can offer. Liability rules also can have tremendous impact on the development and use of technology, and the expert computer world will present that problem in high resolution. Miller & Kerr concede that “our current models for assessing responsibility are not easily applicable in the case of expert robots” and that we have “barely scratched the surface regarding potential liability models.” Nevertheless, they worry that lawyers might gum up the works, such as by advising the roboticists designing the expert computers to ensure that the computers can explain their operations in the event of lawsuits, just as human experts do, which could impede the zeal with which roboticists work to develop better experts.
Watson playing Jeopardy is unlikely to get into any legal tangles, but IBM is not stopping with a win at Jeopardy. The law of expert robots is not that far into our future.
A Challenge for Silicon Valley: Ace My 1L Property Law Exam
We know they can beat human champions at chess and Jeopardy, but can the algorithm gurus in Silicon Valley program a computer to beat my 1L law students on my Property Law exam. I doubt it.
This challenge goes to the heart of the “reinvent law” and “law+tech” movements. There’s no doubt that plenty of the work that lawyers traditionally have performed can be substantially taken over or made far more efficient by computers. E-discovery is the obvious example. And there are domains of law steeped in technical rules and linguistics amenable to algorithm programming. The bizarre world of estates and future interests, for example, could very well be reduced to a program that could crunch through problems on my exam, spitting out the correct descriptions of present possessory estates and future interests about as effectively as any lawyer trained in the field (I would buy that program, give it to my students, and drop that section from the course!). But that’s because it is a field consisting entirely of rules and linguistics, with precisely correct answers to each problem and little room for higher-level reasoning.
Where I think the computers would flunk my exam is on the written essay portion. Bear in mind I do not construct insane factual scenarios on my exams–the kind with aliens invading Earth. I use practical scenarios taken from current news or my practice experience and put the students in situations not unlike those practicing attorneys face. To be sure, domain knowledge is essential for success on these questions, and the doctrine behind it could be stuffed into a computer program. But then what? Some of my questions go something like: “How likely is X to prevail on the Y claim?” or “Is there any problem with what the government has done to Y?” Of course, there is an attack strategy I teach my students for such questions–an algorithm of sorts–which I suppose could also be stuffed into a computer. That is what some Silicon Valley legal shops are trying to do for certain fields, as Lex Machina is for patent litigation. The problem is that the fact scenarios on my exam, as in the real world, can be quite nuanced, or they can be incomplete, requiring a decision tree approach with multiple branchings. Well, maybe Silicon Valley can program that too. But then there are questions asking students to advise clients what to do to solve problems, requiring that they explore and compare a variety of options and devise a game plane. Also doable for computers? Maybe so, but I am getting more skeptical as we go along. The most difficult type of question for me to imagine a computer solving effectively is one requiring students to invent new rules for new kinds of property issues, such as how to treat wind as a property interest given the rise of wind power. These questions require consideration of the theory and policy of property law as well as analogical reasoning to identify rules that work well in similar situations, transport them into the new context, and test how well they fit. Try that, Watson.
I can’t reveal the contents of my exam–it’s not being administered until this Friday and some of my students read this blog. But if anyone in Silicon Valley is up to the challenge, I’ll gladly send it to you and grade the computer’s answers.
Computerized Judging? The Finns are Leading the Way…
A post on Legal Futurology discusses a recent judicial reform report issued in Finland that includes the following recommendation:
In some types of cases the preparation process could be more strongly computer-supported. For example, when the elements of certain crimes are met, the system could automatically offer relevant phrasings as motivations, which could ease up the burden of processing simple high-volume cases, such as drunk driving. This could reduce routine work while at the same time safeguarding the high quality of the decisions.
The overall thrust of this part of the report is that by computerizing the decision-making process, maybe they won’t need as many judges as they have today. Perhaps it is not just practicing lawyers who should get up to speed on the law+tech movement!
Quantum Lawyering
One of the barriers to data storage and processing in existing technology is its binary form: the basic component of computing–the “bits”–are limited to binary encoding as a 0 or a 1. Busting through the binary digital constraint would open up a completely new world of computational power. The March 8 issue of Science includes a special section on the line of research designed to do just that–quantum information processing (QIP). QIP uses quantum mechanics to enable an infinite number of states that could be encoded on each quantum bit, or “qubit.” Given the properties of quantum-mechanical objects, it will be an immense challenge to create the physical architecture to support qubits in computer technology, but if the past of computer science is any indication, we’ll get there.
The chasm between binary and quantum computation technologies captures the limits of the emerging law+tech movement. As a number of previous posts have covered, the law+tech movement is designed to leverage the robust data storage and processing capacities now available to shift some kinds of lawyering services from humans to computers. Many of the tasks that can be shifted are routine, such as e-discovery and automated contract drafting. Some of the tasks, however, are quite sophisticated, such as contract risk assessment and patent litigation planning, and some of the innovations coming out of law+tech are opening up capacities human lawyers could not hope to achieve, such as the data visualizations Ravel Law is experimenting with.
Whether you look at this as good or bad for the legal industry, it’s coming so get used to it. But as much as the law+tech innovators promise to change the way legal services are delivered, they can’t promise what I would call “Quantum Lawyering.” What do I mean by Quantum Lawyering? (more…)
Deep Structure — The Next Generation of Empirical Legal Studies
The use of statistical techniques to tease out empirical patterns in legal contexts has had a profound impact on legal practice and scholarship over the past few decades. From employment discrimination claims to academic studies of judicial voting patterns, we have learned a lot from regression analyses and other statistical applications. But getting at the deep structure of law has been more difficult with that tool kit. The convergence of big data, network theory, data visualization, and vastly enhanced computational capacities is changing that–now we can begin studying law and legal systems in ways that open up new frontiers for practitioners and academics.
As a practical example, sign on to Ravel Law. You will find a simple search field with no instructions. Plug in a term–I used “climate change.” Whereas in Westlaw and Lexis you receive a list of cases, in Ravel Law you receive something very different. Ravel Law gives you the list of cases, to be sure, but it also displays an interactive graphic representation of the citation network of all cases using the search term. The visual representation allows the user effortlessly and instantly to identify cases citing cases, the strength of each case as a citation source for others, and the timeline of cases in the network. So, if a practitioner wants to identify the “big case” in a topic, or to quickly trace the growth of the topic in case law, Ravel Law finds it for you in seconds, whereas piecing that together through traditional searches would take hours and a lot of mental gymnastics.
On a more theoretical level, tools like those used to power Ravel Law can help academics plumb the deeper structure of legal systems. For example, legal concepts and principles can be broken down into finely grained components, as in the way legal research services such as Westlaw and Lexis have developed their “keynote” and “headnote” cataloging systems. These cataloging systems produce hierarchical concept frameworks placing broad legal concepts such as constitutional law and environmental law at the top and then drill down from those broad concepts through successive levels of increasingly narrow subtopics. Michael Bommarito’s study of opinion headnotes in over 23,000 Supreme Court cases illustrates the branching form of what this hierarchy looks like when laid out graphically. (See Michael J. Bommarito II, Exploring Relationships Between Legal Concepts in the United States Supreme Court). As any lawyer knows, however, (more…)
Food for Legal Future Thought: Top 10 Emerging Technologies
A starting point for thinking about the legal future is to spot trends that may develop into scenarios with implications for law, legal practice, and legal education. Earlier this month the World Economic Forum did that for us in its announcement of the Top 10 Emerging Technologies for 2013. One can easily envision legal issues growing out of several of the trends:
- OnLine Electric Vehicles: This involves using wireless technology to power and charge EVs while they move down the road. If the system is widely available, EV batteries can be smaller and the vehicle range extended. Of course, this will requires a massive infusion of new infrastructure in the form of the transmission system in the roads.
- 3-D printing and remote manufacturing: 3-D printing is pretty cool, but already it has led people to ask about labor market implications, world trade implications, and patent and copyright protections. The concept of “open source” 3-D printing, while revolutionary for manufacturing, also would make possible the printing of operable guns with nothing more than a computer and a desktop 3-D printer.
- Self-healing materials: Self-healing materials can repair themselves when cut, torn or otherwise damaged, with no human intervention. To the extent we begin to rely on them for health and safety, how will products liability law respond? (more…)
Law 2050 