Cite as: Eric Engle, Smoke and Mirrors or Science?
Teaching Law with Computers -- a Reply to Cass Sunstein on Artificial
Intelligence and Legal Science, 9 Rich. J.L. &
Tech. 9 (2003).
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
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.
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
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]
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
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]
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,
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
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]
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
characters and words depending on input and correction by the user. [FN17]
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
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:
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.
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.
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
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.
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
is impossible, at present, for a program to represent the law is
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
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.
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:
values to our known case
= "Not Guilty";
our unknown case
example of analogical reasoning
(case2.Argument1 = case1.Argument1)
(Case2.Argument2 == Case1.Argument2)
Alternatively, this could be represented using weighted balances rather
binary values, for
example, presuming the plaintiff has the burden of proof:
If (plaintiff.arguments[weight] >defendant.arguments[weight])
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
as Sunstein argues.
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
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
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.
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.
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
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).
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
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).
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).
"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
"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.
"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.
"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.
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.
"[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.
"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
"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.
Two major avenues of research have emerged over the last two
decades...artificial intelligence (AI) and artificial neural networks
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).
supra note 10.
"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
See generally Sunstein, supra note 5.
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/
See Alavi, supra note 10.
See Sunstein, supra quotation accompanying note 6.
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).
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.
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
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).
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.
James Popple, SHYSTER (SHYSTER is a C source code available under GPL),
(last modified Apr. 30, 1995).
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
this context the CBR system, GREBE is particularly interesting.
Branting's system addresses a major problem that arises in case based
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.
Paul Brna, Imitating Analogical Reasoning, at www.comp.lancs.ac.uk/computing/research/aai-aied/people/paulb/old243prolog/section3_9.html
(Jan. 9, 1996) (using Prolog to illustrate such algorithms).
. See Alavi, supra note 10.
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).
"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.
"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
registers). Thus the
technological change Sunstein awaits would, and can, occur at the
software level and need not occur at the hardware level.
Id. at 2.
See Sunstein, supra quotation accompanying note 9.
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.
Eric Engle, <<La convention franco américaine et la
modélisation du droit fiscal par l'informatique >>,131
Fiscalite Europeenne-- Droit International Des Affaires (2003).