Eric Allen Engle,
and Mirrors or Science? Teaching Law with Computers -- a
Reply to Cass Sunstein on Artificial Intelligence and
9 Rich. J.L. & Tech. 9
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Copyright © Eric Allen Engle
The application of computer science in the
law has largely, and productively, centered on educational
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 2
The majority of the work involving AI in the law, as the majority
of work in AI generally, has focused on developing expert systems.
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
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. 5
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 6
(moral) choice 7
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, 8
at least not yet. 9
As we shall
see, this position is simply untenable. 10
There is some cause to focus, as Sunstein
appears to, 11
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. 12
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."
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
On the other hand, "dynamic" computer programs
generate their own rules of production and are consequently
Programs which "learn" by dynamically developing rules of
production do exist 14
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. 15
The most well-known work in AI that
actively "learns" is in the field of neural networks. 16
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. 17
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
because he argues that they cannot derive principles from
cases-which, as this article will show, is false. 19
for deriving new principles from existing cases do exist 20
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" 21
SHYSTER, a case-based legal expert system. 22
Aikenhead's arguments are much
more refined and computationally accurate 23
Programmatic representations of inductive
inference, like most computational problems, are, in fact, trivial
computational sense of the word). For example, presume that we
have three known cases, each having three attributes with the
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
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 25
through all combinations, first favoring, then disfavoring, the
outcome and using different weights and comparing the result to
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. 26
would have us ignore this. 27
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.
Sunstein hedges his position. First, he
argues that his position that an analogical inference engine is
impossible cannot, in fact, be verified, 28
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
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:
//Assign values to our
Case1.Argument1 = 1;
Case1.Argument2 = 1;
Case1.Argument3 = 1;
Case1.Outcome = "Not
//Trivial example of
if (case2.Argument1 =
if (Case2.Argument2 ==
Alternatively, this could be represented using
weighted balances rather than
for example, presuming the plaintiff has the burden of proof:
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
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 31
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 32
) 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.
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 Eurpaeische Rechtspolitik.
1. 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).
2. 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
3. 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).
4. 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).
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.
6. "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.
7. "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.
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.
9. "[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.
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.
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.
12. 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
Alavi, supra note 10.
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
14. See id.
generally Sunstein, supra note 5.
16. 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/.
17. See Alavi,
supra note 10.
Sunstein, supra quotation accompanying note 6.
19. 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).
20. 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.
Akinori Abe, Abductive Analogical Reasoning, at http://www.kecl.ntt.co.jp/icl/about/ave/aar.html
(last visited **
Nov. 25, 2002).
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.
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);
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), at http://cs.anu.edu.au/software/shyster/
(last modified Apr. 30, 1995).
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
Aikenhead, supra note 19.
In this context the CBR system, GREBE is
particularly interesting. Branting's system addresses a major
problem that arises in case based reasoning; of
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.
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.
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).
27. "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
28. "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.
29. Id. at 2.
Sunstein, supra quotation accompanying note 9.
31. 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,
convention franco américaine et la modélisation du droit fiscal
par l'informatique >>,131 Fiscalite Europeenne-- Droit
International Des Affaires (2003).