9 Rich. J.L. & Tech. 9
*1 The application of computer science in the law has largely, and productively, centered on educational programs [FN1]
and programs generating and managing databases and data management.
Some limited work, however, has been done in the use of artificial
intelligence ("AI") to present models [FN2] of legal decision-making. [FN3]
The majority of the work involving AI in the law, as the majority of
work in AI generally, has focused on developing expert systems. [FN4] An expert system attempts to solve one
problem, or one class of problems well and should
be distinguished from general systems, which seek to solve any problem.
While databases and didactic applications involve the most productive
work being done, AI presents more complex and more interesting problems
having the potential to further increase productivity. Therefore, while
AI may still be in its infancy (as was all of computer science until
twenty years ago), its intellectual vistas are potentially limitless.
*2
It was thus with mixed reactions that I read a recent article by Cass
Sunstein, arguing that AI in law is of very limited utility and, at
present, can only solve very limited classes of legal problems.
Sunstein likens computer AI in law at present to a glorified LEXIS. [FN5]
This is not the case because AI does more than automatically sort data.
Sunstein argues that computers cannot reason analogically because a
computer cannot make an evaluative [FN6] (moral) choice [FN7]
and thus cannot infer new rules of production from old rules of
production. Sunstein argues that because computers cannot reason
analogically, they cannot develop AI, [FN8] at least not yet. [FN9] As we shall see, this position is simply untenable. [FN10]
*3 There is some cause to focus, as Sunstein appears to, [FN11]
on expert systems rather than general systems: the problems facing the
elaboration of an expert system are much more manageable than those
facing the development of a general system. However, AI programs are
not simply split between expert systems and general systems but also between
"static" systems with fixed (pre-programmed) rules of production and
"dynamic" systems which develop their own rules of production based
upon experience. [FN12]
*4
Static AI programs, such as the one I present here, use invariable
pre-programmed rules of production. They do not learn because their
rules of production are "fixed." For example,
though
chess programs are the most often-cited example of successful AI, they
are, in fact, an example of "static" AI (albeit highly-developed). This
is because most chess programs do not adapt their algorithms to take
advantage of the weaknesses of their opponent, but rather use a
pre-programmed algorithm.On the other hand, "dynamic" computer programs generate their own rules of production and are consequently 'trainable." [FN13] Programs which "learn" by dynamically developing rules of production do exist [FN14]
and truly "learn." Such programs must derive principles (for example,
the shape of a letter) from cases (a given set of experiences that are
either provided by the user or are pre-programmed)--which is the very
type of reasoning that Sunstein argues a computer cannot do. [FN15]
*5 The most well-known work in AI that actively "learns" is in the field of neural networks. [FN16]
Neural networks are best known for being used to recognize characters.
This field of AI is well-developed and, therefore, no longer in its
infancy. Programs such as Apple's Inkwell do indeed "learn" to recognize characters and words depending on input and correction by the user. [FN17]
*6
Rather than focusing on the split between "active" programs, which
"learn," and "static" programs, which do not, Sunstein focuses on a
split between analogical reasoning (inductive logic) and deductive
reasoning. Deductive reasoning is necessarily true but develops no new
propositions. Inductive reasoning is not necessarily true but develops
new propositions. Sunstein argues that computer programs, or at least
computer programs in law, cannot learn to properly apply analogical
reasoning [FN18] because he argues that they cannot derive principles from cases-which, as this article will show, is false. [FN19] Algorithms for deriving new principles from existing cases do exist [FN20]
and usually invoke iterative and/or pseudo-random functions or
abduction to do so. As Aikenhead notes, "[n]umerous systems have been
constructed that simulate aspects of legal analogizing" [FN21] including SHYSTER, a case-based legal expert system. [FN22] Aikenhead's arguments are much more refined and computationally accurate [FN23] than Sunstein's.
*7 Programmatic representations of inductive inference, like most computational problems, are, in fact, trivial [FN24]
(in the computational sense of the word). For example, presume that we
have three known cases, each having three attributes with the following
values:
CASE VALUES
-------------
I 1,2,3
II A,B,C
III A,2,3
Given a Case IV, with values 1, 2, C, analogical reasoning would
compare the values and determine that Case IV is most similar to Case I
and apply the same rule in Case IV.
*8
Inductive amplification does not merely apply existing rules by analogy
to a new case. Rather, it derives a new rule from existing cases. It is
this type of reasoning which Sunstein argues a computer is incapable of
doing. Using the same example, the derivation of a new rule would be
that any new case will be decided according to the rule used in the
case with the greatest number of similar elements. Such a rule could be
initially programmed into the computer as a "static" rule of
production, or could be "learned" through trial and error as a dynamic
rule of production.
*9
Let us consider the example of a node in a neural network with three
inputs that is suddenly given a new fourth input. The new input must be
tested and controlled to determine whether the new input is to favor or
disfavor a certain outcome and to determine what weight should be
applied to the new input. That control can either be pre-programmed
or, more efficiently, be supplied by the user. Statically, the program
would be given at least two rules of production. Say that new factors
shall have, for example, 1/n weight (where n equals the total number of
factors to be considered) and that factor n (the new factor) should
favor the party with no burden of proof unless instructed otherwise. To
these rules of production could be added a third human "oversight"
function: by running, for example, three trials where the human
oversees the outcome, verifying or denying the computer response, and
programmatically applies the rule of production that the human taught
the computer, the computer would be able to correctly apply the new
rule of production and apply it simulating analogical reasoning. Such a
control function could be pre-programmed, however, by iterating [FN25]
through all combinations, first favoring, then disfavoring, the outcome
and using different weights and comparing the result to existing cases.
*10
If Sunstein's position is, so far as programming computers goes,
untenable, what about its validity in law? There are valid grounds for
questioning Sunstein's assumptions about law. Legal scholars should
recognize that, although the common law relies heavily on analogical
reasoning, the civil law relies equally heavily on deductive reasoning.
[FN26] Sunstein would have us ignore this. [FN27] Since Sunstein does not appear to question the ability of a program to represent deductive reasoning, it seems his claim that
it is impossible, at present, for a program to represent the law is ill-considered.
*11
Sunstein hedges his position. First, he argues that his position that
an analogical inference engine is impossible cannot, in fact, be
verified, [FN28] which takes his opinion out of the realm of science. Second, Sunstein argues that "things might change." [FN29]
These hedges appear to be contradictory. Sunstein has said that his
claim cannot be verified or falsified, yet implies that new technology
may one day permit machines to model analogical reasoning. [FN30]
Even if we could grant both these (contradictory) hedges, Sunstein's
argument goes too far and can be demonstrated to be erroneous using a
trivial example of analogical reasoning.
*12
Let us presume that two Cases with three elements each will be
similarly decided if two of the elements are the same (that is, if the
two cases are analogically similar). Programmatically, analogical
reasoning could be represented thus:
//Assign values to our known case
Case1.Argument1 = 1;
Case1.Argument2 = 1;
Case1.Argument3 = 1;
Case1.Outcome = "Not Guilty";
Case2.Outcome = "Unknown";
//Trivial example of analogical reasoning
if (case2.Argument1 = case1.Argument1)
{
if (Case2.Argument2== case1.Argument2)
{
Case2.Outcome == Case1.Outcome;
}
if (Case2.Argument3== case1.Argument3)
{
Case2.Outcome ==Case1.Outcome;
}
}
if (Case2.Argument2 == Case1.Argument2)
{
if (Case2.Argument3== Case1.Argument3)
{
Case2.Outcome == Case1.Outcome;
}
}Alternatively, this could be represented using weighted balances rather than
binary values, for example, presuming the plaintiff has the burden of proof:
If (plaintiff.arguments[weight] >defendant.arguments[weight])
{
decisionForPlaintiff();
}
*13
These reasons give us cause to question Sunstein, both as programmer
and legal theorist. Sunstein would have done better to argue that
computation of law is impossible because building and training a neural
network to operate as a general system is too complex. Such an argument
might hold water. Complexity in programming a general system of AI can
be found in the fact that creation of a general system would first
require a natural language parser. However, such parsers have existed
for at least twenty years, and are constantly improving. Presuming that
an adequate parser could be found or built, the next difficulty would
be developing self-learning neural networks. This is difficult, but
certainly not impossible. Such a neural network should include a human
"control" to test and correct the engine's inferences, as that would
generate a better engine more quickly than one based purely on
pre-programmed rules of production. The next difficulty would be to
generate a sufficiently large field of cases to teach the engine.
Again, this is not an insurmountable task. Though this task would, like
adapting a natural language parser, be time-consuming. A final
difficulty might be the computational speed or memory required. This is
the least of the problems, as enormous expert
systems are well within the computational power of contemporary desktop
computers. Although general systems in AI are the exception, they are
not computationally impossible and would not require some new break
through in microprocessor architecture or memory [FN31] as Sunstein argues.
*14
This contentious introduction is intended to set the stage for my very
unambitious static inference engine. I have written a program to assist
in the teaching (and perhaps practice) of tort law. This program
(unlike my next) is well within the hedges that Sunstein places on his
bets. It does not use a neural network, and does not "learn" a new
system of rules (but it could have by relying on either a neural-node
model or using a tree model with either head or tail recursion).
Instead, the program provided is "static." I have given a set of rules
of production (which represent the basics of tort law) and then the
program asks the user a series of questions to determine whether a tort
has taken place and giving reasons for its decision. The program
essentially uses a series of "binary" tests as rules of production.
*15
Another approach to static AI modeling of law (which I used in a
program to determine the tax residence of a person according to the
French-U.S. tax treaty [FN32])
uses a weighted "balancing" approach. One advantage to the binary
method is that when the law can be represented as "either/or," it is
highly methodologically defensable as a model of law. In contrast, the
law only rarely quantifies the weight given to its nodes
when balancing competing interests. If there were any computational
problems in generating AI models of law, it is, in my opinion, here:
the quantification of factors that may be quantifiable but generally
are not quantified by courts. Even this difficulty is not
insurmountable. The law sometimes uses quantifiable economic data to
calculate the weight of factors in legal balancing tests. So, using a "
balancing" test is computationally and legally defensible.
*16
The program is basically self-explanatory and includes a brief essay on
tort law, which, along with the source code, is reproduced below. The
program was written in xTalk, a derivative of Pascal, using the
MetaCard engine. I chose xTalk rather than C or a C- derived language
because it is "pseudo" English and because the auto-formatting of the
metaTalk editor makes the source code readable even for
non-programmers. Given the current computational speed and power of
CPUs, I see no real advantage to developing AI using a lower-level
language such as C or assembler. However, the fact that there are
plenty of C AI libraries explains why programmers may wish to port them
to Pascal or xTalk, so that non-programmers can correctly assess the
utility of AI in representing the law for purposes of teaching,
research, and even legal practice. A great deal of useful work can be
done using computers to model law either in education or data
management or in legal practice including, eventually, general AI
systems. Whether the position that Professor Sunstein takes is defensible will be revealed in time.
[FNa1]. Eric Engle, J.D.,
D.E.A., L.L.M., teaches at the University of Bremen (Lehrbeauftagter)
and is also a research fellow at Zentrum Fuer Eurpaeische Rechtspolitik.
[FN1]. See, e.g., Dan Hunter,
Teaching Artificial Intelligence to Law Students, 3 Law Tech. J. 3
(Oct. 1994), available at http:// www.law.warwick.ac.uk/ltj/3-3h.html
(discussing the methodological problems involved, especially the
problems of developing sylabi for teaching law and AI).
[FN2]. See Alan L. Tyree, Fred
Keller Studies Intellectual Property, at
http://www.law.usyd.edu.au/~alant/alta92-1.html (last modified Dec. 20,
1997) (discussing self-paced instructional programs).
[FN3]. See, e.g., Alan L.
Tyree, FINDER: An Expert System, at http://
www.law.usyd.edu.au/~alant/aulsa85.html (last modified Dec. 20, 1997).
[FN4]. See, e.g., Carol E. Brown and Daniel E. O'Leary, Introduction to Artificial Intelligence and Expert Systems, at http://
accounting.rutgers.edu/raw/aies/www.bus.orst.edu/faculty/brownc/es_tutor/es_ tutor.htm (last modified 1994).
[FN5]. "My conclusion is that
artificial intelligence is, in the domain of legal reasoning, a kind of
upscale LEXIS or WESTLAW-- bearing, perhaps, the same relationship to
these services as LEXIS and WESTLAW have to Shepherd's." Cass R.
Sunstein, Of Artificial Intelligence and Legal Reasoning 7 (Chicago
Public Law and Legal Theory Working Paper No. 18, 2001), available at
http://
www.law.uchicago.edu/academics/publiclaw/resources/18.crs.computers.pdf.
[FN6]. "I have emphasized that
those who cannot make evaluative arguments cannot engage in analogical
reasoning as it occurs in law. Computer programs do not yet reason
analogically." Id. at 9.
[FN7]. "I believe that the
strong version [of claims for machine-based AI] is wrong, because it
misses a central point about analogical reasoning: its inevitably
evaluative, value-driven character." Id. at 5.
[FN8]. "Can computers, or
artificial intelligence, reason by analogy? This essay urges that they
cannot, because they are unable to engage in the crucial task of
identifying the normative principle that links or separates cases." Id.
at 2. Sunstein appears to be grappling with the
so-called law of Hume (the idea that moral choice cannot be deduced,
but is instead arbitrary), yet does not address Hume. E.g., "[S]ince
HYPO can only retrieve cases, and identify similarities and
differences, HYPO cannot really reason analogically. The reason is that
HYPO has no special expertise is [sic] making good evaluative
judgments. Indeed, there is no reason to think that HYPO can make
evaluative judgments at all." Id. at 6.
[FN9]. "[W]e cannot exclude
the possibility that eventually, computer programs will be able both to
generate competing principles for analogical reasoning and to give
grounds for thinking that one or another principle is best." Id. at 8.
[FN10]. "A trained [neural]
network is capable of producing correct responses for the input data it
has already 'seen', and is also capable of 'generalisation', namely the
ability to guess correctly the output for inputs it has never seen."
Farrukh Alavi, A Survey of Neural Networks: Part I, 2 IOPWE Circular
(Int'l Org. of Pakistani Women Eng'rs), (July 1997), available at
http://www.iopwe.org/JUL97/neural1.html.
[FN11]. "What I am going to urge here is that there is a weak and strong
version of the claims for artificial intelligence
in legal reasoning; that we should accept the weak version; and that we
should reject the strong version, because it is based on an inadequate
account of what legal reasoning is." Sunstein, supra note 5, at 4.
While it is true that we can look at the fact that general systems are
much more difficult to construct than expert systems to support this
statement, Sunstein does not. Nor does Sunstein support this point by
noting the difference between "dynamic" AI, which truly learns and
generates new rules of production based on iterative experience, from
"static" AI, which merely represents pre-programmed rules of
production. Rather Sunstein supports this point, which is for the two
aforementioned reasons tenable, with a third point, which is not;
Sunstein relies on a dichotomy of deductive reasoning and analogical
reasoning to explain the limitations of AI at present. In fact, the
problem Sunstein has identified is either that of Mill (that inductive
ampliative arguments are not necessary, only probable, as opposed to
deductive arguments, which are either necessary or invalid) or that of
Hume. Hume considers the logic and science amoral and implies that
moral choice is arbitrary. In fact, this author disagrees with the
popular representation of Hume, but that point is outside the scope of
this paper.
[FN12]. Two major avenues of
research have emerged over the last two decades...artificial
intelligence (AI) and artificial neural networks (ANNs). While there are some similarities in both the
origins and scope of both of these disciplines, there are also
fundamental differences, particularly in the level of human
intervention required for a working system: AI requires that all the
relevant information be pre-programmed into a database, whereas ANNs
can learn any required data autonomously. Consequently, AI expert
systems are today used in applications where the underlying knowledge
base does not significantly change with time (e.g. medical diagnostic
systems), and ANNs are more suitable when the input dataset can evolve
with time (e.g. real-time control systems).
Alavi, supra note 10.
[FN13]. "In 1986, Rumelhart,
Hinton and Williams published the 'back-propagation' algorithm, which
showed that it was possible to train a multi-layer neural architecture
using a simple iterative procedure." Id.
[FN14]. See id.
[FN15]. See generally Sunstein, supra note 5.
[FN16]. See, e.g., Dan
Hunter, Commercialising Legal Neural Networks, 2 J. Info. Law
&Tech. (May 7, 1996), at http://
elj.warwick.ac.uk/jilt/artifint/2hunter/.
[FN17]. See Alavi, supra note 10.
[FN18]. See Sunstein, supra quotation accompanying note 6.
[FN19]. See Michael
Aikenhead, A Discourse on Law and Artificial Intelligence, 5 Law Tech.
J.1 (June 1996), available at http://
www.law.warwick.ac.uk/ltj/5-1c.html) (discussing the different theories
about modeling law using AI in the law, including the use of models for
analogical reasoning).
[FN20]. If a knowledge base
does not have all of the necessary clauses for reasoning, ordinary
hypothetical reasoning systems are unable to explain observations. In
this case, it is necessary to explain such observations by abductive
reasoning, supplemental reasoning, or approximate reasoning. In fact,
it is somewhat difficult to find clauses to explain an observation
without hints being given.
Therefore,
I use an abductive strategy (CMS) to find missing clauses, and to
generate plausible hypotheses to explain an observation from these
clauses while referring to other clauses in the knowledge base. In this
page, I show two types of inferences and combines [sic] them. One is a
type of approximate inference that explains an observation using
clauses that are analogous to abduced clauses without which the
inference would fail. The other is a type of exact inference that
explains an observation by generating clauses that are analogous to
clauses in the knowledge base. Akinori Abe, Abductive Analogical Reasoning, at http:// www.kecl.ntt.co.jp/icl/about/ave/aar.html (last visited Nov. 25, 2002).
[FN21]. Michael Aikenhead,
Legal Principles and Analogical Reasoning, Proc. Sixth Int'l Conf.
Artificial Intelligence & Law: Poster Proc., 1-10 (extended
abstract available at
http://www.dur.ac.uk/~dla0www/centre/ic6_exab.html); Simulation in
Legal Education (with Widdison R.C. and Allen T.F.W.), 5 Int'l J.L.
&Info. Tech. 279-307.
[FN22]. James Popple,
SHYSTER (SHYSTER is a C source code available under GPL), at
http://cs.anu.edu.au/software/shyster/ (last modified Apr. 30, 1995).
[FN23]. Aikenhead
acknowledges limitations in existing models but does not categorically
dismiss them and, unlike Sunstein, presents critiques which will lead
future work in this field. For example, Aikenhead writes:
In
this context the CBR system, GREBE is particularly interesting.
Branting's system addresses a major problem that arises in case based
reasoning; of determining when two cases are sufficiently
similar to allow analogical reasoning to occur. For two cases to be
similar, their constituent elements must be sufficiently similar. How
is the existence of this similarity determined? Branting's innovation
is to allow GREBE to recursively use precedents to generate arguments
as to why elements of cases are similar. Once the elements of cases can
be said to be similar, the cases themselves can be regarded as similar.
The system thus generates an argument as to why cases are similar by
recursively generating an argument as to why constituent elements of
cases are similar. While GREBE only operates in the domain of CBR and
is not a complete argumentation system it does illustrate how useful
argumentation processes can be self-referential. What is needed is an
extension of this approach; applying the full theory of legal
discourse. In such an approach, arguments would recursively be
generated as to how and why rules or cases should apply. Aikenhead, supra note 19.
[FN24]. Paul Brna, Imitating
Analogical Reasoning, at http://
www.comp.lancs.ac.uk/computing/research/aai-aied/people/paulb/old243prolog/section3_9.html
(Jan. 9, 1996) (using Prolog to illustrate such algorithms).
[FN25]. See Alavi, supra note 10.
[FN26]. Wim Voermans et al.,
Introducing: AI and Law, Inst. for Language Tech. and Artificial
Intelligence, at http://cwis.kub.nl/~
fdl/research/ti/docs/think/3-2/intro.htm (last visited Nov. 25, 2002).
[FN27]. "What is legal
reasoning? Let us agree that it is often analogical." Sunstein, supra
note 5, at 5. In civil law jurisdictions legal reasoning is more often
deductive than analogical. Therefore, there is good reason to reject
Sunstein's assumption about legal reasoning.
[FN28]. "We should reject
the strong version [of legal reasoning via AI] because artificial
intelligence is, in principle, incapable of doing what the strong
version requires (there is no way to answer that question, in
principle)...." Sunstein, supra note 5, at 4. This directly contradicts
a later hedge of Sunstein's when he argues that future technology may
make computer AI possible. Either it is impossible and/or unverifiable
or it is possible either with existing or future technology. In fact,
while the speed of microprocessors and the amount of data which can be
stored has changed radically in the last twenty years, processor
architecutre (a bus and registers) remains basically the same as that
of forty years ago (just with larger busses and more registers). Thus the technological change
Sunstein awaits would, and can, occur at the software level and need
not occur at the hardware level.
[FN29]. Id. at 2.
[FN30]. See Sunstein, supra quotation accompanying note 9.
[FN31]. Such a break
through, while not necessary (neural networks can be programmed using
existing CPUs), is possible. "As an information-processing system, the
brain is to be viewed as an asynchronous massively-parallel distributed
computing structure, quite distinct from the synchronous sequential von
Neumann model that is the basis for most of today's CPUs." Alavi, supra
note10.
[FN32]. Eric Engle,
<<La convention franco américaine et la modélisation du droit
fiscal par l'informatique >>,131 Fiscalite Europeenne-- Droit
International Des Affaires (2003).
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