What is Legal Technology?

In my opinion, AI & law desperately needs a makeover. One simple but effective way for the field to reinvent itself is rebranding, and the best label for this I can think of is Legal Technology (oikeusteknologia, rättsteknologi, ret(t)steknologi, õigustehnoloogia, R/rechtstechnologie, technologie juridique, tecnologia giuridica, юридическая технология &c).

As someone who has followed and worked in language technology for about two decades now, I see AI & law as being now in the same state as language technology was in the early 1990s. I have presented some lessons learned on how to approach real-world problems at the detail level in my robo-judge paper, so I won't go into them here. Instead, my proposal here looks into language technology as a field that has succesfully reinvented itself a couple of times already. Early on, it was only known as natural language processing (NLP) as a subfield of AI and a form of basic research rarely with any concrete application in mind. (A notable exception to this is machine translation, which also happens to be older than the term AI itself. More on that in a separate post as well as an article written jointly by me and Anniina Real Soon Now.) Then came computational linguistics, which was centered on using computational models and techniques as a tool for linguistic research. (This is where I think AI & law is now.) Of these, in particular corpus linguistics has become mainstream in virtually all subfields of linguistics, but other computational methods are now widely used outside computational linguistics proper as well. Through the 1990s computational linguistics also started to find its way into commercial applications in domains such as language checking, information retrieval, text-to-speech and vice versa, dialogue systems, and machine translation. As these real-world applications started to generate increasingly important research questions in their own right, language technology was born.

"Legal technology" as a term is not my invention. For example, in the US there has been a bicoastal biannual conference called LegalTech® since 2006. As far as I know, most of the technologies presented there are are not all that interesting from an AI & law perspective, with topics such as case management and billing platforms, synchronizing your BlackBerry with your Outlook and stuff like that, and whatever new cruft Westlaw and LexisNexis have come up with each year.

More to the point are for example the LawTechCamps arranged by Daniel Martin Katz (of Computational Legal Studies) and others in June 2011 and next week in Toronto and in the end of June in London. There is also a growing number of start-up companies in the field at least in the US, as listed just the other day on the eLawyering blog. Most of the start-ups listed seem to be working on applications having to do with contracts (possibly a sign of flock mentality from the venture capital side?). Contracts are also the target of the only out legal tech start-up I know of here in Finland, Sopima. With a large number of companies on the same domain fighting over the same market from somewhat different perspectives, it is clear that only some of these companies will be able to succeed (at least as far as the US companies are concerned, Europe is a different kettle of fish because of the different legal culture(s) and the prevalence of non-English languages). The best products have to address a real-world problem and solve it well and efficiently. Usability is another key success factor, and it still seems to be generally neglected in legal IT. Just because a certain design is a possible way to do something does not mean it is the best way (indeed it rarely is, though at least it usually is not quite this bad; required reading: Donald Norman's The Design of Everyday Things, MIT Press 1989). In particular, just replicating ancient practices from the age of pen and paper (and secretaries) and possibly adding some bells and whistles is a true and tried pattern, unfortunately. And the result is an application that takes a week-long course just to get started with it. All the same, the technically best solution does not necessarily win the game. In the end, it all boils down to the viability of the business model and the ability to make it into a reality. (Here's a convenient rule of thumb: marketing costs money, selling makes money. Close early, close often.)

So how can the AI & law community contribute to the impending legal technology boom? One approach is to take an existing, reasonably well-developed Good Old-Fashioned AI & law technology, and to find a real-life legal problem which it could possibly solve. (I'm afraid I can't come up with an example.) The other approach is to take an existing problem (= market need) in the legal community, a problem of the kind that should be solvable by computing, and looking around all over the place in computer science in search of that solution. (Here e-discovery is a prime example, though it does not travel well, and performance-wise it is quite disappointing by the language technology metrics I'm used to but at least it is still equally reliable yet faster and cheaper than people doing the same job.) Since language is in a key role in law, language technology is one obvious place to look at but it should definitely not be the only one for any legal tech company. I'm sure the next 20 years will be a lot more interesting (and profitable) for the field than the past 20.

Zombies and robots and cyclists, oh my!

Inspired by today's Decision by Rector Magnificus Thomas Wilhelmsson, I have made one of my own as well. Rather than keep struggling with writing a traditional monograph, I have decided to leverage my already published peer-reviewed work in the form of an article dissertation (like all the cool kids today).

That is, my thesis will consist of the obligatory introduction (which also includes a couple of things I could first publish as standalone articles but can't be bothered, namely looking at WEBSOM from a legal perspective and a business planresearch programme for cognitive legal theory), and the following articles, the first four of which are already published and the fifth is a written version (now with 100% more cognitive semantics!) of the paper I presented last summer at GikII:

• MOSONG: A Fuzzy Logic Model of Trade Mark Similarity
• From Spelling Checkers to Robot Judges? Some Implications of Normativity in Language Technology and AI & Law
• Dual-Process Cognition and Legal Reasoning
• Grundnorm as Conceptual Bootstrapping: An Ontogenetic Perspective
• Zombies and Legal Personhood
• Burdens of Proof, Probabilities, and Alberto Contador

This shouldn't take more than a couple of weekends per article plus some more for the introduction, so I might actually be able to defend my thesis in 2013 after all. I have a bunch of other article ideas in store as well, but they'll have to be put on hold for now. Stay tuned!

What is innovation?

I'm being profiled on WIN-novators later this spring. As an preview, here's my take on the question in the title of this post:

I suppose there are basically two possible aspects on innovation. The first is taking something preexisting and familiar and making it just a little better. The other alternative is a radical break with tradition and doing things differently altogether. In a sense this echoes the distinction between normal science and scientific revolutions introduced into the philosophy of science by Thomas Kuhn. But in reality (rather than philosophy) these aspects are complementary rather than dichotomous.

To take a concrete example: the shift from, say, a 32 nanometre to a 22 nanometre process in semiconductor manufacturing is not immediately visible for the computer user and from this perspective it may seem like yet another meaningless number in the computer specifications orat best a small incremental change. From the manufacturing perspective, on the other hand, shedding those extra nanometres has required enormous technological advances. One additional constraint onthe design is also the amount of heat being generated into a smaller and smaller space that still has to be dissipated through a cross-section of comparable size, leading to the invention of 'darksilicon' (powering down parts of a chip that are unused at any given moment). And on the other hand, the cumulative effect of such changes in terms of processing power, storage capacity and so on (Moore's lawand all that) enable new approaches to all kinds of problems thatwould have been quite impossible a decade or two ago. After all, eventoday's smartphones are more powerful than supercomputers in most ofthe 1980s.

The ability to deal with immense amounts of data in real time isdefinitely one of the two biggest driving forces for artificialintelligence in the foreseeable future. Recently I looked into the history of machine translation, and one of the earliest systems actually took twice as long just to do what amounts to looking up each individual word in a dictionary and stringing them together into an approximation of a translation as what it would take for a human translator to produce a correct translation. A system like Google Translate, on the other hand, has ginormous collections of multilingual documents with aligned language elements and uses them together with some heavy statistical processing to do the same job and produce at least something understandable if not correct in justfractions of a second. Oh, and the other driving force? Bio-inspired AI, or seeing how Nature has solved a given problem and trying to reproduce that in an artificial design.

As for my own work, I try to keep these different perspectives in mind, and while I occasionally like to throw words like 'robot judge' around, it is more as an abstract target (and of course also asprovocation) rather than as something I am actually concretely interested in implementing. But it is certainly helpful in trying to keep in mind the whole range of issues potentially involved in working with legal AI, and not just the issues du jour the research community finds interesting at the moment. In my opinion, one part of the problem is also that mainstream legal theory does not study law and legal reasoning in particular as a form of cognitive activity and has managed to all but ignore all the scientific progress made in both linguistics and psychology over the past fifty years, and in much of my work I take theories from those disciplines and try to apply them to questions of legal theory in a very general sense (mostly because almost nobody else seems to be doing it). Still, the best way forward for me seems to be trying to model some very small corner of the legal system using some particular technique (and I think I'm stuck with fuzzy logic at least until I've finished my dissertation) to see whether it works and then trying to see whether there are some broader conclusions to be drawn based on it. And in the best case it might even do something useful (read: marketable) at the same time. In this respect, AI & law seems to be about twenty years behind language technology.

We don't particularly need robot judges, but for example judicial decision-support systems could help actual judges in making correct and consistent decisions more efficiently and reliably, thus perhaps enabling them to spend more time on cases where uniquely human capabilities are really required. And if at the same time technology also revolutionizes the way legal services are provided (as predicted by Richard Susskind in particular), maybe the parties do not even have to go to court in the first place.

Augmenting Man

"The combination of machines and ICT has brought exponential development into the engineering world. As a result we see the emergence of autonomous machines. The complexity of tasks as well as the complexity of environments where these machines can work is steadily increasing. We can build cars that can navigate autonomously through city traffic finding destinations without any human intervention. Therefore, it is fair to say that machines have, on a functional level, already reached cognitive abilities comparable to horses or dogs. But this is not the end of development. Soon there will be no type of manual labor in which machines will not outperform humans. This is technically already true today. Currently, machines are merely held back by economic and societal constraints. The weakest of these constraints is the cost of hardware. Moore’s Law guarantees that computing power that today can only be found in supercomputers will be available in pocket sized devices in little a more than a decade. Some other constraints are more difficult to overcome. The more powerful and more complex a machine is, the more damage it can potentially create. This is the reason there are no self-steering cars on the roads yet. We have suggested a path of best practices and ethics to improve machines and reduce intentionally malicious behavior. Nevertheless, even those best practices leave us with a residual risk. This residual risk is not necessarily small. It may indeed be so large that certain types of machines will not be able to enter the market because of liability concerns. This limitation will only be overcome by the creation of an ultimate machine. For human parents responsibility and liability for a child ends with it becoming an adult. Similarly a machine can become an ultimate machine by emancipating itself from its manufacturer/owner and indeed becoming a distinct legal or even social entity. Interestingly, this can be done by creating a legal construct around this ultimate machine that in itself has economical value.

Nevertheless, the big question remains: how will our societies hold up to this rapid change? For example, currently our entire tax and social system, indeed most of our culture, is centered on the concept of work as the means of creating one’s livelihood. For example, the European Union has set a goal of increasing the part of the population (between 15 and 64 years of age) in gainful employment to 70 per cent up to 2010. Yet when machines are able to perform manual labor cheaper and more efficiently than humans, what jobs will remain? Former US Secretary of Labor, Robert Reich, assumes that manual labor will eventually be replaced completely by machines. Nevertheless he argues that there will still be a high demand for a human work force. These new workers will have to be highly educated and trained “symbolic analysts” – lawyers, doctors, journalists, consultants, and the like – which create value beyond mere manufacturing. However currently only a fraction of the labor force is capable of performing these jobs. Even though goverments have stated their intention to increase investment in education it is questionable whether this goal can be achieved for everyone. And even if it were possible, the advancement in information technology is not restricted to manual labor. Machines have augmented the physical performance of man to the point were he becomes superfluos. The same augmentation is also taking place with our cognitive abilities. The famous quote of the computer being a “bicycle for the mind” becomes evident when we consider the vast amount of data a single person can analyze with the help of a personal computer. Therefore, the observation that machines in the long run are not destroying jobs but creating new ones is merely that; an observation and not a law. There might well be a threshold of automation that changes the rules of the game entirely. If that should happen this would be one aspect in which we have to change our culture radically. In any case, how well we are prepared for these new machines will determin the social acceptance and ultimately the cost of the transition. Since development is still gradual, there will be several years left to create new practises. There is likely not a simple nor a single answer. The convergence of disciplines and the accelerating speed of technological progress will require a holistic approach and result in ad-hoc solutions. Fortunately, we can start learning about the problem and its solutions already today. After all, the future is already here, just not equally distributed."

William Brace, Anniina Huttunen, Vesa Kantola, Jakke Kulovesi, Lorenz Lechner, Kari Silvennoinen and Jukka Manner,
"Augmenting Man,"
in Bit Bang, Rays to the Future, Yrjö Neuvo and Sami Ylönen, Helsinki: Helsinki University Print, 2009, p. 236-263

File sharing + Robots = "Low Orbit Server Drones"

Have you heard about the Pirate Bay's latest idea? They are planning Low Orbit Server Stations (LOSS). In other words, they would like to host "parts of their site in GPS-controlled drones, instead of old-fashioned data centers." Due to a court decision my operator Elisa has blocked its customers access to the Pirate Bay. Consequently, I am not able to check what they say about the subject, but below you can find some information:

The Pirate Bay Attacks Censorship With Low Orbit Server Drone

The Pirate Bay Planning "Low Orbit Server Drones"

And I thought that there is no way to combine file sharing and robot studies...

How I Became Interested in Intelligent Systems?

During the academic year 2008-2009 I participated in the Bit Bang post-graduate course.

“Bit Bang – Rays to the Future is a post-graduate cross-disciplinary course on the broad long-term impacts of information and communications technologies on lifestyles, society and businesses. It includes 22 students selected from three units making the upcoming Aalto University: Helsinki University of Technology (TKK), Helsinki School of Economics (HSE) and University of Art and Design Helsinki (UIAH). Bit Bang is a part of the MIDE (Multidisciplinary Institute of Digitalisation andEnergy) research program, which the Helsinki University of Technology has started as part of its 100 years celebration of university level education and research. Professor Yrjö Neuvo, MIDE program leader, Nokia’s former Chief Technology Officer, is the force behind this course.”

We wrote a joint publication based on the fall and spring group works. The book was published in co-operation with Sitra, the Finnish Innovation Fund:

http://lib.tkk.fi/Reports/2009/isbn9789522480781.pdf

During the fall term my group wrote about the processor and memory a book chapter “The Digital Evolution – From Impossible to Spectacular” and in spring we were given topic intelligent machines. Finally, our book chapter was titled “Augmenting Man”. And that's the way it all started. (To be continued...)

Current Projects and a Book on Linking

Currently, I participate in the Graduate School Law in a Changing World:

http://www.helsinki.fi/omm/english/index.htm

"LCW graduate school covers all fields of legal studies, from various branches of positive law to general jurisprudential studies. Each doctoral student will get acquainted with the europeanisation and the globalisation of law... LCW provides the doctoral students with a systematic 4-year research training programme."

So far, we have had great fun together!

In addition, I am a member in a research project titled "New technologies in the content production and usage" funded by the Helsingin Sanomat Foundation:

http://www.hssaatio.fi/images/stories/Pihlajarinne_Tiivistelm_pitk_suomi.pdf

Our principal investigator LL.D., docent Taina Pihlajarinne just published a book about linking (A Permission to Link) . It is available only in Finnish:

http://www.efokus.fi/flash/lupa_linkittaa/#/1/