21st Century Legal Informatics: Part 1, Introduction [Cross Post MJB II Blog]

Dan and I have written and spoken on legal informatics many times.  Inevitably these conversations come to the same cut-and-paste list of informatics examples from legal search/retrieval and decision making.  It’s struck me that these examples fall into two categories.  The first category sits firmly in the 20th century, while the second category belongs in the 22nd century.  I’ll support my argument below and conclude this introduction with a lead into what I’d like to call 21st century law.  The rest of this series will provide living, breathing examples, leveraging new technologies and new paradigms that are useful today, in the 21stcentury.

20th Century Legal Informatics – Computers as Libraries


Ask a typical lawyer how informatics affects their practice, and, if you’re lucky, they might mention that salary infographic their friend emailed them a few weeks ago.  Data, modeling, statistics, and visualization might be seen as cute toys, but not real tools.   Ask a typical lawyer how search affects them, however, and they’ll have no trouble producing a list of five-figure-per-seat services like Lexis, West, CCH, or RIA.  So why is it that only search has enjoyed such a successful impact on practice?  Is it because other informatics tools just aren’t useful?

My opinion on the matter is that search is the only informatics tool that fits into the current legal paradigm, which I’ll call the library modellaw is a field of humans interpreting words, words live on documents, and documents live in libraries.  Legal training focuses on reading and interpreting words and documents.  Success in practice depends on locating, interpreting, and communicating information.  Therefore, for a new tool to be accepted by lawyers, it must complement this library model to allow lawyers to locate, interpret, and communicate faster and better.  From this standpoint, it’s easy to see why search has succeeded – it is a tool easily applied to language that facilitates the traditional library model of law.

The library model of law, while Aristotelian in ideals, still conveniently abstracts the underlying world away in an unmeasurable Platonic black box.  As such, words, not numbers and functions, are the most convenient way to communicate states.  It is hard to argue against this on any practical grounds.  Despite the progress of science and the acceptance of measurement in modern society, many quantities integral to legal questions will remain unmeasurable.  The best example, of course, is intent.  Did he mean to kill her?  Did they mean to commit fraud?  While we have the ability to detect intent at a vague level through fMRIs, would we ever consent as a society to have “intent implants” that continuously measured and recorded our every impulse?  The average gut reaction to Minority Report suggests not.

Philosophy aside, we can agree that human language is the most practical medium to practice law, and that search is the most practical tool to facilitate work in this medium.  Computers are simply portable, accessible, and easily searched libraries, and the labor of law is still primarily conducted by humans.  While technology can improve productivity, our creativity is constrained by the underlying library model of law, as well as the low expectations set by the 1L’s first venture into the stacks of the quiet, dusty library.

22nd Century Legal Informatics – Computers as Lawyers


The second category is best understood through a hope and a struggle: IBM Watson and the International Association for Artificial Intelligence and Law (IAAIL).  Watson embodies the hopes of 22nd century legal informatics, in which law is a field of computers building and interpreting models to make legal decisions.  In the days and weeks after Watson’s victory against other Jeopardy! contents, there was a slew of articles on the automation of information services like law; even the ABA chimed in.

Likewise, the IAAIL embodies the struggles and failures of this model.  Arguably one of the most forward thinking associations in academia, the IAAIL and its members have been presenting data models and ontologies, search methods, expert systems, and judicial reasoning for more than 30 years.  Their approach to empiricism and rationalism in law predates acceptance in many other social fields.  Many of their ideas might provide a significant improvement in the quality and cost of legal outcomes in situations ranging from negotation to arbitration to litigation.  However, as a participant and former member myself, I will readily admit that the IAAIL has mostly failed to introduce these ideas into the mainstream of legal practice.  Experts, not expert systems, still dominate evidence and testimony.  Justices and judges, never computers, reason and decree from the bench.  Even the elegant models of argumentation and reasoning are mostly ignored by legal educators.

The reason for this failure, in my opinion, is that these ideas, for all their worth, do not complement the library model of law outlined above.  So long as this is the model of education and practice, many of IAAIL’s ideas will remain just that – ideas, drifting in the clouds (and I don’t mean Amazon Web Services).  Progress will come, but slowly and in steps, measured by successes like Watson’s stunning and public expert system.  And so we can agree for now that the future is promising, but certainly not here.  Computers are not and will not functionally replace humans in most legal contexts any time soon.

21st Century Legal Informatics – Computers and Lawyers


What can we do in the meantime while our robotic overlords are still incubating?  How can we improve the quality and cost of law despite the constraints of our weak, organic bodies and slow-moving societies?  I’d like to argue that the way forward is through 21st century law, not 20th or 22nd century law, as identified by the principles below:

  1. Balance.  Neither humans nor computers alone will provide optimal outcomes.  21st century law must allocate these two assets based on a solid understanding of process workflow and technology.
  2. Measure, but not too much.  Measure whenever and wherever possible, but avoid promoting measurement when it isn’t the solution.  Measurement drives further formalization of law, either by inductively determining better rules or by better evaluating rules.
  3. Change, but not too much.  The library model of law is a natural paradigm for a species as expressive as ours.  However, when we can measure, our languages are often worse approximations than mathematical models and numbers.  Focus on cases where accurate, accepted, and easily evaluated models can be built, such as finance, and slowly chip away at the library model through these successes.
  4. Aim high.  Don’t let expectations based on the library model set your bar for success.  Apply and experiment with whatever technology is available to retrieve faster and analyze better.  Just because Lexis and headnotes are faster and easier than a trip to the stacks doesn’t mean search and research shouldn’t be faster and better.

Sounds pretty easy, right?  Stay tuned for examples to come, including e-Discovery, search, and legal rule exploration.

[Cross Posted from MichaelBommarito.com Blog]

Legal Futures.co.uk Conference – New ways to Practise Law

My thanks to Neil Rose and all of the LegalFutures.co.uk conference organizers and speakers – it was a very interesting conference. As a byproduct of the modifications to the UK Legal Services Act, change is on the march in the UK legal services market. Keep your eye on these developments — as they may be coming to the US — sooner rather than later.

Judges in Jeopardy? – Actually – It is Lawyers in Jeopardy

While I really appreciate the spirit of this article, I have to say that the question posed by the author is not actually the critical one.  As noted by Larry Ribstein in his post “Lawyers in Jeopardy” — the primary question raised by Watson and other forms of soft to medium artificial intelligence is their impact on the market for legal services. In thinking about this broader problem, I am haunted by the line from There Will be Blood – “I Drink Your Milkshake.”  In this metaphor, technology is the straw and the legal information engineer is Daniel Day Lewis.

It is worth noting that although high-end offerings such as Watson represent a looming threat to a variety of professional services — one need not look to something as lofty as Watson to realize the future is likely to be turbulent. Law’s Information Revolution is already underway and it is a revolution in data and a revolution in software.  Software is eating the world and the market for legal services has already been impacted.  This is only the beginning.  We are at the very cusp of a data driven revolution that will usher in new fields such as Quantitative Legal Prediction (which I have discussed here).

Pressure on Big Law will continue.  Simply consider the extent to which large institutional clients are growing in their sophistication.  These clients are developing the data-streams necessary to effectively challenge their legal bills.  Whether this challenge is coming from corporate procurement departments, corporate law departments or with the aid of third parties — the times they are indeed a-changin’.

A variety of intermediary consulting firms and legal informatics companies have developed a robust business advising corporate clients how to find various arbitrage opportunities in the legal services market. One of the best examples is TyMetrix — who has recently leveraged more than $4 billion in legal spend data to help General Counsels and their corporate law departments drive down legal costs.  Indeed, The Real Rate Report has made a huge splash (if you do know what I am talking about – I suggest you learn – because it is a pretty big deal).

Adapting Specialized Legal Metadata to the Digital Environment: The Code of Federal Regulations Parallel Table of Authorities and Rules [ Bruce and Richards ICAIL 2011]

Rock / Paper / Scissors – Man v. Machine (as t→∞ you are not likely to win) [via NY Times]

From the site … “A truly random game of Rock-Paper-Scissors would result in a statistical tie with each player winning, tying and losing one-third of the time …  However, people are not truly random and thus can be studied and analyzed. While this computer won’t win all rounds, over time it can exploit a person’s tendencies and patterns to gain an advantage over its opponent.

Computers mimic human reasoning by building on simple rules and statistical averages. Test your strategy against the computer in this rock-paper-scissors game illustrating basic artificial intelligence. Choose from two different modes: novice, where the computer learns to play from scratch, and veteran, where the computer pits over 200,000 rounds of previous experience against you.”

Time to dust off your random seedpseudorandom number generators … good luck!

The AI Revolution Is On [ Via Wired Magazine ]

From the Full Article: “AI researchers began to devise a raft of new techniques that were decidedly not modeled on human intelligence. By using probability-based algorithms to derive meaning from huge amounts of data, researchers discovered that they didn’t need to teach a computer how to accomplish a task; they could just show it what people did and let the machine figure out how to emulate that behavior under similar circumstances. … They don’t possess anything like human intelligence and certainly couldn’t pass a Turing test. But they represent a new forefront in the field of artificial intelligence. Today’s AI doesn’t try to re-create the brain. Instead, it uses machine learning, massive data sets, sophisticated sensors, and clever algorithms to master discrete tasks. Examples can be found everywhere …”

Law as a Complex Adaptive System: An Updated Reading List / Syllabus

As a new semester is here at Michigan CSCS, I have made several revisions to the content of our global reading list for the Computational Legal Studies Working Group. The content of this interdisciplinary reading list features work from economics, physics, sociology, biology, computer science, political science, public policy, theoretical and empirical legal studies and applied math. I wanted to highlight this reading list for anyone who is interesting in learning more about the state of the literature in this interdisciplinary space.  Also, for those interested in learning model implementation, please consult my my slides from the 2010 ICPSR Course Introduction to Computing for Complex Systems. Feel free to email me if you have any questions.

Riders on a Swarm — Might Mimicking the Behavior of Ants, Bees & Birds Be the Key to Artificial Intelligence?

This week’s issue of the Economist has an interesting article entitled Riders on a Swarm. Among other things, the article discusses how attempts to computationally model ant, bee and bird behavior have offered insight into major problems in artificial intelligence.

For those not familiar, the examples discussed within the article are classic models in the science of complex systems. For example, here is the Netlogo implementation of bird flocking. It will run in your browser but requires Java 4.1 or higher. If you decide to take a look — please click setup – then go to make the model run. Once inside the Netlogo GUI, you can explore how various parameter configurations impact the model’s outcomes.

One of the major insights of the bird flocking model is how random starting conditions and local behavioral rules can lead to the emergence of observed behavioral patterns that appear (at least on first glance) to be orchestrated by some sort of top down command structure.

This is, of course, not the case. The model is bottom up and not top down. Both the simplicity and the bottom up flavor of the model are apparent when you explore the model’s code. For those interested, I will take a second and plug the slides from my ICPSR class. In the class, I dedicated about an hour of class time to bird flocking model. Click here for the slides. In the slides, I walk through some of the important features of the code (discussion starts on slide 16).