Preface

Robert H Lee *

In the paper that follows, Hans Andersson offers a definition of computational law as “law that

works like software”. He seeks to distinguish it from the broader field of legal informatics, which touches on

all forms of IT that affect law, including statistical approaches to legal problems that require massive data

sets, Bayesian inference, and often, natural language processing. This latter aspect of legal informatics

resembles the “big data” trend that is all the rage in contemporary Silicon Valley, where big data companies

analyze consumer behavior, among many other subjects.

Hans also seeks to distinguish computational law from mechanized lawyering, which is the IT

management of legal practice process, as well as from computational compliance (also called compliance

automation), which concerns itself with how to make regulatory/rules/administrative compliance “work like

software”. This includes, for example, IT tools that aid users in complying with administrative requirements,

such as filling out DMV applications or filing electronic IRS 1040 tax forms. Like computational law,

computational compliance requires the explicit representation of rules embedded in code, not merely the

general use of IT.

While these areas are not the focus of the following paper, this is not to say that they lack

considerable utility. The intellectual challenges and potential benefits of big data informatics are far from

obscure— just do a search on TechCrunch or Venture Beat for “big data”. And computational compliance,

in particular, can have profound effects on efficiency and error reduction with the simplest of tools. The

architecture of online tax filing software, as an example, is trivial in terms of software complexity (in

contrast with the higher math and other tools that big data analysis require). In fact, one could argue that if

software and computers had not progressed so much in the past decades, if the Moore’s Law doubling

period was 10 years instead of 1.5-2 years, then computational compliance would be more much more

popular as an area of study in industry and academia.

This is because meaningful computational compliance tools can be built with megabytes of code

run on kilohertz processors. For instance, consider the representation of the rule that business meal and

entertainment expenses can be deducted at a 50% rate from gross income. This representation can be done,

at a basic level, with just a few bytes of data. Of course, interpretation of the meaning and boundary

conditions of all these terms, such as the meaning and scope of “entertainment”, does require more than

this. The key point, however, is that explicit representation of the base rule can be done with a trivial

amount of code. And this level or representation in itself has considerably utility, in that it can be part of a

Andersson—Computational Law—Preface ! /! 1 2

tax engine that allows users to walk through the process of tax calculation, much as current tax filing

software does.

As one of many other examples, statutory form representation is another area in which

computational compliance can be implemented with very simple tools. At the federal, state and municipal

level, many regulatory rules are promulgated which allow users to automatically comply with legal

requirements by following simple guidelines. These rules are sometimes turned into statutory forms by

governments and third parties, so that users may be assured of prima facie or de facto comply by filling out

these forms. While such forms generally have very few explicit components, they are rarely semantically

represented, meta-tagged, or marked up to allow for suh computational implementation. They are, at best,

made available online in non-semantic text or graphical formats. Therefore each user is forced to manually

read through, interpret and fill out the required data, whereas a small amount of computational work could

render each instance of use much simpler by semantic representation.

So please enjoy the journey which Hans is about to take you, including the intellectual subtleties of

the metes and bounds of computational law. Also realize, however, that even a superficial understanding of

the concepts contained herein could have a profound impact on the way in which law is practiced,

implemented, and made available to the general public, if this understanding was to have a wide audience.

!* Robert H Lee

Research Fellow

CodeX – The Stanford Center for Legal Informatics, codex.stanford.edu

A joint effort of Stanford Law School and the Stanford Computer Science Department

Andersson—Computational Law—Preface ! /! 2 2

Computational Law:

Law That Works Like Software

Hans Andersson

February 10, 2014

Computation could revolutionize law1 by making law work like software, transforming the

practice of law from a high-touch enterprise to a low-touch one, that is, from one requiring

pervasive human intervention, which introduces coextensively pervasive errors and ine�cien-

cies, to one automated to the same extent that execution of debugged and tested software

is automated. Insofar as law computes itself, legal practice will become less expensive, more

accessible, and more reliable.

Literature relevant to computational law has largely failed to support developments in

practice and industry because theoreticians and practitioners lack a shared understanding of

technical problems in computational law and how to attack them.2 This paper narrows the

gap between theory and practice by laying a common groundwork and identifying directions

for further research and development.3 This paper aims to maintain a level of technical rigor

1See Karl Branting, Legal Reasoning and Artificial Intelligence: How Computers “Think” Like Lawyers, 8U. Chi. L. Sch. Roundtable 1, *13 (2001) (arguing that computational law will revolutionize the practiceof law over the next few decades); Jeanne Eicks, Successful Modern Curricula Join Law and Technology,Educating Superior Legal Professionals § 5.01–5.02 (Oliver Goodenough & Marc Lauritsen, eds.,2012), available at http://www.law.harvard.edu/programs/plp/pdf/006-03358 03358-ch0005.pdf (last visited2013/08/02) (arguing that “technology is reshaping the legal field”).

2Cf. Eicks, supra note 1 (“[M]ost law schools do not o↵er students a legal education infused withinformation technology concepts. Students who arrive at law school with a computer science background orfrom a technical field experience a depth to their legal education beyond that experienced by average lawstudents.”).

3Kevin Ashley, Professor of Law and of Intelligent Systems at the University of Pittsburg, explains that,in 2013, “the current focus of AI and Law research”, an areas of law relating closely to computational

1

not only that academics and researchers can thoughtfully engage but also that practitioners

and innovators can usefully apply.

1 What do we mean by “computational law”?

This paper proposes defining “computational law” by reference to “law that

works like software”. This definition casts computational law as more computer

science—even programming, coding, basic “hacking”—than vaunted legal the-

ory. Today and in the near future, computational law is and will be a way of practicing

law; some day, computational law might become essentially synonymous with “the practice

of law”. The work of lawyers might come to look like the work of software engineers in a

specialized domain.

As the Stanford Logic Group has observed, computational law concerns (a) primarily

two agendas—(i) formal representations and (ii) automated reasoning—(b) relating to laws,

regulations, rules, and contracts (c) in electronically-mediated domains.4 In the present

context, “formal representations” means structuring abstract ontologies5 as concrete data6;

“automated reasoning” means algorithmically inferring conclusions (e.g., applications of laws

to facts) from given premises (points of fact) and rules (points of law).7 Computational law,

law, consists in “the development of a unified computational framework for non-monotonic, defeasible legalreasoning using argument schema that incorporate and recast the logic-deductive, case-based, and value-based approaches to assessing legal claims”. Kevin Ashley, Teaching Law and Digital Age Legal Practicewith an AI and Law Seminar, 88 Chi.-Kent L. Rev. 783, 796 (2013).

4Stanford Computational Law, http://complaw.stanford.edu (last accessed July 15, 2013); see alsoKevin Ashley, 8 U. Chi. L. Sch. Roundtable 1, supra note 1, *1 (“Computational models of analyticallegal reasoning are comprised [sic] of a knowledge representation and an inference mechanism. The knowledgerepresentation captures some important aspects of legal knowledge. And the inference mechanisms arealgorithms that enable a program to use those elements of legal knowledge that are represented in orderto solve problems.”); Ashley, supra note 3, 786–7 (arriving at a similar definition in the related context ofartificial intelligence and the law).

5See Andrew Clearwater, 10 J. Marshall Rev. Intell. Prop. L. 181, 182 (2010) (“[A]n ontology[. . . ] is a hierarchical organization of a knowledge domain that contains entities and their relations.”; citingWebster’s Third New Int’l Dict. Engl. Lang. 1577 (2002); and citing WordNet Search, PrincetonU., http://wordnetweb.princeton.edu/perl/webwn?s=ontology (last visited Sept. 30, 2010)).

6Cf. Jonathan Jenkins, What can information technology do for law?, 21 Harv. J. L. & Tech. 589,607 (2008) (mentioning “[n]ew ways of constructing arguments, new methods for analyzing large sets of legaldata, and new systems for representing that data”)

7Cf. Harry Surden, Computable Contracts, 46 U.C. Davis L. Rev. 629, 668–9 (2012) (narrating

2

as something like the subsumption of legal practice into software engineering, thus reflects

a traditional bifurcation of computer science into structures and algorithms.8 Later, the

present section will consider the implications of this bifurcation of computational law.

With respect to the software-architecture pattern of “model–view–control”,9 computa-

tional law subsists primarily in model (representation of laws, regulations, rules, and con-

tracts) and control (automated legal reasoning), not in presentation (interfaces, configura-

tions, etc.), much as the human practice of law concerns how to think like a lawyer, not

how to look and act like a lawyer. Thinking of computational law in terms of model and

control promotes separation of concerns (viz., how to represent law computably and how

to reason about computable law), a “divide and conquer” strategy that facilitates reuse of

basic components of computational-law systems (e.g., on the one hand, a computable rep-

resentation of a section of the U.S. Code and, on the other hand, an engine that interprets

such computable law) from one application to another.10

The present definition of computational law describes very little like what one sees in

the world today.11 This paper rejects as misguiding a potentially tempting definition that

would hastily find computational law in any system whose users inputting, through what-

an example in which a computer determines whether a student’s major course of study comports with ahypothetical condition).

8See generally Niklaus Wirth, Algorithms + Data Structures = Programs (1978).9See generally Steve Burbeck, Applications Programming in Smalltalk-80(TM): How to use Model-

View-Controller (MVC), U. Ill. Urbana–Champaign Dept. Comp. Sci., http://st-www.cs.illinois.edu/users/smarch/st-docs/mvc.html (last visited 2013/07/27) (“In the MVC paradigm[,] theuser input, the modeling of the external world, and the visual feedback to the user are explicitly separatedand handled by three types of object, each specialized for its task. The view manages the graphical and/ortextual output to the portion of the bitmapped display that is allocated to its application. The controller

interprets the mouse and keyboard inputs from the user, commanding the model and/or the view to changeas appropriate. Finally, the model manages the behavior and data of the application domain, respondsto requests for information about its state (usually from the view), and responds to instructions to changestate (usually from the controller).”); see also rj45 (pseudonym), Simple Example of MVC [. . . ] DesignPattern for Abstraction, CodeProject 2008/08/08, http://www.codeproject.com/Articles/25057/Simple-Example-of-MVC-Model-View-Controller-Design (last visited 2013/07/27) (illustrating model–view–controlwith a simple example).

10See Philip Laplante, What Every Engineer Should Know about Software Engineering 4.2.1(p. 85) (2007), available at http://books.google.com/books?id=pFHYk0KWAEgC (last visited 2013/07/27)(discussing coupling and cohesion).

11But see Ashley, 8 U. Chi. L. Sch. Roundtable 1, supra note 1, *2 (mentioning HYPO, CATO, andGREBE, all of which seem, from Ashley’s discussion, apt examples of computational law).

3

ever interface such system might present, a legal query to obtain a legal response would find

themselves unable, given only the response, to determine whether a legal professional—a hu-

man puppeteer hiding behind the computer interface—had provided the system’s response.12

This paper rejects that test because it would include systems that mimic only the outward

behaviors associated with computational law while clearing none of the technological hurdles

to making law work like software.

The rejected definition might include, for example, Intuit’s TurboTax.13 TurboTax poses

a series of tax-related questions to users, then completes and submits the users’ tax returns.

While that behavior outwardly mimics how a tax professional serves a client, TurboTax ap-

pears to engage in neither formally representing laws, regulations, rules, and/or contracts

nor automatically reasoning about substantive questions of law, except to arrive at formulaic

recommendations mostly hard-wired prior to runtime. TurboTax appears not to demonstrate

law working like software. Withholding the label of computational law from TurboTax re-

minds us that the field looks for innovations far more general and more thoroughgoing.

The proposed definition of computational law, by contrast, might attach to the CATO ap-

plication developed by Kevin Ashley.14 Ashley’s CATO appears to go far beyond TurboTax :

CATO analyzes case law for decisive factors, then uses those factors to evaluate strengths

and weaknesses for both a plainti↵ and a respondent in a new fact pattern.15 CATO ap-

pears to rely upon both formal representations of fact patterns and automated reasoning

according to predefined but general algorithms that imitate the way legal professionals rea-

son.16 The present definition of computational law extends beyond CATO, accommodating

12Cf. Graham Oppy & David Dowe, The Turing Test, The Stanford Encyclopedia of Philoso-phy (Edward Zalta, ed., 2011/spring), http://plato.stanford.edu/archives/spr2011/entries/turing-test/ (dis-cussing the test of artificial intelligence, useful elsewhere in computer science, that inspired this paper’s wordsof caution against cross-application).

13Cf. Love & Genesereth, infra note 42, *2 (citing TurboTax as an example of computational law).14Kevin D. Ashley, U. Pitt. Learn’g Res. & Dev’t Cent., http://www.lrdc.pitt.edu/ashley/ (last

visited 2013/07/28).15Ashley, supra note 11, *12.16See generally Ashley, supra note 11, *2–*7 (explaining how CATO operates on a hypothetical fact

pattern).

4

computational-law systems outside analogical contexts most pertinent to the courtroom.17

Legal scholarship, especially in preeminent law journals and reviews, has often misunder-

stood the essentially technical/technological18 field of computational law, misrepresented its

realistic prospects and limitations, and misdirected the discussion with detached scholarship

that one might find in a journal of humanities and social sciences.19 Computer scientists

have taken little interest in computational law because computational law, from the perspec-

tive of computer science, consists of not cutting-edge research but its application; resources

and rewards for computer-science research lie elsewhere than in computational law.20 The

emerging field of computational law will therefore likely prove the birthright of altogether

17Cf. Branting, supra note 1, *14 (“[T]here are quite a few di↵erent participants in the legal system[,] and[. . . ] for each one of these participants there may be various di↵erent legal tasks[,] each requiring a separatecomputational model. By the participants, I mean we have the members of the public, clients, attorneys,judges, clerks, [and] legislators[.]”).

18The author contrasts “technical” with, for example, “philosophical”.19Cf. Gordon, infra note 20, *4–5 (“Most models of legal reasoning which have precision su�cient to serve

as a foundation for legal information technology have been developed by colleagues working in computerscience departments or research institutes, not law faculties[.] [. . . ] Presumably, the main reason so muchfundamental research on AI and Law has been conducted by computer scientists rather than lawyers issimply because few lawyers in the past have had su�cient expertise in computer science methods to buildcomputational models of legal reasoning.”). The author cites the following as examples of scholarly mis-understanding, misrepresentation, and/or misdirection: Betsy Cooper, Judges in Jeopardy!: Could IBM’sWatson Beat Courts at Their Own Game?, 121 Yale L.J. Online 87, *88 (2011) (“Here I propose howWatson could apply his skills in a legal environment: by helping textualists interpret statutes. New textu-alists believe in reducing the discretion of judges in analyzing statutes. Thus, they advocate for relativelyformulaic and systematic interpretative rules. How better to limit the risk of normative judgments creepinginto statutory interpretation than by allowing a computer to do the work?”); Cass Sunstein, Of ArtificialIntelligence and Legal Reasoning, 8 U. Chi. L. Sch. Roundtable 29, 31 (2001) (positing as a relevant—even central—factor in evaluating a certain computational-law system whether, in such system, “artificialintelligence is actually engaging in legal reasoning”); Cyrus Tata, The Application of Judicial Intelligenceand ‘Rules’ to Systems Supporting Discretionary Judicial Decision-Making, 6 Artif. Intel. & L. 203,223–5 (1998) (Cyrus Tata directs the following criticism broadly against the use of artificial intelligence inlegal reasoning: “Cyrus Tata has argued that legal decision-making requires more than simple legal rulesand formal logic.” Jonathan Jenkins, What can information technology do for law?, 21 Harv. J. L. &Tech. 589, 604 (2008)); Stephen McJohn, Artificial Legal Intelligence, 12 Harv. J.L. & Tech. 241,243–4 (suggesting artificial neural networks as one of the subfields of artificial intelligence most helpful tocomputational law for the following reason: “The network can be ‘trained’ by adjusting the interconnectionsor adding new connections in the network, until a given input produces the desired output. Once the networkis properly adjusted, it should yield the correct output for future inputs. Because a neural network shouldlearn a rule based on a number of cases, and should be flexible enough to adjust to new cases, the applicationof the concept to legal reasoning has been readily noted.”).

20See Thomas Gordon, Artificial Intelligence and Legal Theory at Law Schools, Ar-tif. Intel. & L. Educ. 53–58 (Hajime Yoshino & al., eds., 2005), available athttp://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.88.6437&rep=rep1&type=pdf (last visited2013/08/02), *5 (“The legal domain, indeed every application domain, is only interesting to computerscience departments or institutes so long as it serves the interests of their primary research topics[.]”).

5

another cohort: people who care less about juxtaposing Karl Marx and Oswald Spengler in

a single “see generally”21 and less about advancing computer science than about seizing op-

portunities to make personal fortunes—computational law belongs to engineers and, most of

all, to entrepreneurs, whether in business ventures, in private practice, or in public service.22

2 What dimensions has computational law?

(1) The present definition of computational law contemplates a range of implementa-

tions, from simply ticking through a checklist of necessary elements of a cause of action

(“specific computational law”) to delivering nuanced determinations based on complex fact

patterns and legal regimes (“general computational law”).23 One can imagine a most general

system, which we will call a “Codex Machine”, that would comprise substantially the entire

legal corpus and that could field a query on any legal subject and, provided unlimited time

and space and whatever informational resources the system might require, not only provide

a response as cogent as that of a legal professional in the same situation but also arrive

at that conclusion through a series of steps that resemble the approach a legal professional

would have taken. The Codex Machine, at present only a figment of imagination, serves as

a technological touchstone by which to measure this field’s progress.

(2) The present definition of computational law purposefully embraces any conceivable

configuration of hardware and software, from integrated, embedded systems to modu-

lar, freestanding ones. Consider, on the one hand, an integrated hardware–software system

embedded in a car to determine whether the car’s driver is obeying tra�c laws at a given

21Stephen McJohn, Artificial Legal Intelligence, 12 Harv. J.L. & Tech. 241, 254 n.49 (criticizingPamela Gray, Artificial Legal Intelligence (1997), for historicizing).

22See generally Rene Newman Knake, Cultivating Learners Who Will Invent the Future of Law Practice,38 Ohio N.U.L. Rev. 847 (arguing that innovators will inherit the law).

23See also Ben Goertzel & Cassio Pannachin, Cognitive Technologies: Artificial General Intel-ligence vi (2007), available at http://people.inf.elte.hu/csizsekp/ai/books/artificial-general-intelligence-cognitive-technologies.9783540237334.27156.pdf (last visited 2013/07/23) (defining “artificial general intel-ligence”, this paper’s inspiration for neologisms “specific computational law” and “general computationallaw”).

6

point in time24—such a system’s pronouncements, by whatever interface delivered to the

driver, might prove indistinguishable from pronouncements a legal professional might render

to the driver via the same interface. On the other hand, a platform coded purely in software

and extended piecemeal with modules corresponding to substantive areas of law25 could be

designed to render armchair advice indistinguishable from that of a legal professional on a

range of subjects and fact patterns. This second, software-only system—as much as the first,

hardware–software one—could demonstrate law working like software.

(3) Measured along a normative axis, computational law could serve descrip-

tively and/or prescriptively.26 The output of a system serving the first purpose describes

law codified elsewhere and adjudicated by other authorities, while the output of a system serv-

ing the second purpose prescribes the law itself.27 A descriptive system might help a judge

assess the strengths and weaknesses of arguments in a case, while a prescriptive system might

replace the human judge outright, handing down the system’s own decisions, by a process

that might or might not resemble how courts have traditionally pretended to operate. To

revisit the preceding paragraph’s example of a system integrated into a car monitoring the

car’s driver, such a system might either alert the driver to potential violations (descrip-

tive) or issue tickets based on the system’s own determinations (prescriptive). Descriptive

and prescriptive systems of computational law motivate divergent normative/philosophical

debates, all of which the present paper avoids.

(4) Overall, the field of computational law distinguishes itself from two siblings:

24Cf. Joann Muller, Get Ready for Talking Cars that Don’t Crash and Get You to Work on Time, Forbes2012/08/21, available at http://www.forbes.com/sites/joannmuller/2012/08/21/get-ready-for-talking-cars-that-dont-crash-and-get-you-to-work-on-time/ (last visited 2013/07/27) (“By communicating with othercars, as well as with road signs and tra�c lights, vehicles will [. . . ] be able to avoid most accidents[.]”)

25Cf. The Legal Assistant (software services), http://www.thelegalassistant.com (o↵ering separatemodules for real estate, personal injury, criminal law, family law, etc.).

26To the author’s knowledge, these useful characterizations have not yet appeared in the literature.27See also Settlement talks in the Microsoft case hinge on a question: Are the laws

of government or software supreme?, The New York Times 1999/12/06, available athttp://www.nytimes.com/1999/12/06/business/technology-digital-commerce-settlement-talks-microsoft-case-hinge-question-are.html (citing Lawrence Lessig, Code and Other Laws of Cyberspace (1999),on the idea that “when commercial code begins to determine the Internet’s architecture, [the code] createsa kind of privatized law”).

7

Legal Informatics refers to “the study of information, its technology, and its implications

and impact in the field of law”28; the field encompasses the likes of “how informa-

tion technology helps lawyers”, especially through statistical analysis of issues and

cases that lawyers face.29 Software services that help lawyers find relevant cases and

statutes, that aggregate data about jury verdicts and awards, and/or that compute

lawyer “batting averages” (e.g., Lexis and Westlaw) all fall squarely within the field of

legal informatics.30

Mechanized Lawyering will describe the machine-oriented automation of a set of work-

flows specific to lawyering,31 for instance, due diligence in the context of a corporate

acquisition.32 Mechanized lawyering might (but not necessarily) employ legal infor-

matics, but whereas legal informatics focuses on the role of information, mechanized

lawyering focuses on the processes by which lawyers practice.

Computational Compliance (alternatively, “Compliance Automation”) means creating

interfaces that facilitate compliance with specific laws, regulations, and rules.33 Com-

pliance automation does not necessarily require the heavy-duty programmatic machin-

ery of legal informatics, and though compliance automation implicates the law, unlike

mechanized lawyering, this field does not necessarily reflect work that a legal profes-

sional would normally perform. TurboTax, which serves as a front-end interface to tax

documents and assists lay users in completing them correctly, exemplifies compliance

automation.28Christopher Hinson, Legal Informatics: Opportunities for Information Science, 46 J. Educ. Libr. &

Info. Sci. 2, p. 134 (2005), available at http://www.jstor.org/stable/40323866 (last visited July 15, 2013).29Cf. AI am the law, The Economist 2005/03/10, http://www.economist.com/node/3714082 (discussing

“SplitUp”, software that predicts substantive outcomes of divorce proceedings).30See Branting, supra note 1, *14 (“[T]he dominant models, e.g., the LEXIS/Westlaw kind of model, don’t

use much in the way of [artificial intelligence].”)31To the author’s knowledge, this useful term has not yet appeared in the literature.32Cf. DiligenceEngine, https://diligenceengine.com (This software service promises to automate certain

aspects of contract review.).33To the author’s knowledge, this idea and term originated from Robert Lee, a fellow at Codex, the

Stanford Center for Legal Informatics.

8

Computational law intersects—or at least abuts—legal informatics, mechanized lawyer-

ing, and compliance automation.34 But these sibling fields do not necessarily involve the

essential elements of law working like software, namely, (1) formal representations of legal

regulations, rules, or contracts and/or (2) automated legal reasoning; therefore, legal infor-

matics, mechanized lawyering, and compliance automation fall at least partly outside the

domain of computational law. Computational law focuses less on information technology

than does legal informatics, less on automatic workflows than does mechanized lawyering,

and more on the aspects of law that make computational law like the clockwork of formal

logic with practical application as far-reaching as that of basic algebra.

(5) Computational law touches a number of fields in computer science, in-

cluding natural-language processing,35 machine learning,36 and artificial intelligence.37 Aca-

demics have applied these subfields of computer science to law since the middle of the twen-

tieth century.38 Yet each of these ancillary fields one can distinguish from computational

law—computational law ceases to depend on natural-language processing, machine learning,

or any particular kind of artificial intelligence the moment law begins to work on its own like

software. For that reason, computational law stands apart, a proper field demanding new

high-level39 algorithms and structures.

34The author has personally encountered arguments casting computational law as almost a strict subsetof legal informatics, a conceptualization the present paper rejects.

35See, e.g., Surden, supra note 7, 633 n.10 (2012) (implying the relevance of natural-language processingto computational law).

36See Jonathan Jenkins, What can information technology do for law?, 21 Harv. J. L. & Tech. 589,597 (2008) (discussing machine learning in the context of “litigation support systems”).

37Stuart Nagel, Literature on Computer Software and Legal Decision Making, 82 L. Libr. J. 749, 751 n.6(1990) (citing Proceedings of the First International Conference on Artificial Intelligenceand Law (1987), which discusses “law-related software programs”).

38See generally Je↵rey Meldman, A Structural Model for Computer-Aided Legal Analysis, 6 Rutgers J.Comps. & L. 27 (1977).

39Readers unfamiliar with the computer-science distinction between high-level and low-levelconcerns can find a brief, accurate, helpful overview at High- and low-level, Wikipedia,http://en.wikipedia.org/wiki/High- and low-level (last visited 2013/08/03).

9

3 How can we approach computational law?

Practical application of computational law must utilize both special algorithms and spe-

cial structures, but this paper proposes, recognizes, and asserts a distinction between two

approaches: a structure-centric “semantic” approach and a algorithm-centric “syntactic”

approach. The distinction, to which a previous section of this paper alluded and which

somewhat reflects the “space–time tradeo↵” of computer science,40 carries crucial implica-

tions for real-world applications of computational law because choosing to emphasize either

structures or algorithms can lessen the need to rely on the complement. In other words:

Emphasizing structural solutions may require more space but leave fewer problems to solve

algorithmically, while emphasizing algorithmic solutions may require more time but leave

fewer problems to solve structurally.41 The following discussion evaluates both approaches.

(1) The structure-centric, semantic approach to computational law revolves around

meanings of legal concepts and interpretations of the factual state of the world.42 The se-

mantic approach aims first and foremost to represent laws, regulations, rules, and contracts

in data formats readily readable by machines. This approach employs, for example, such

markup languages43 as XML44 to, one could say, “preprocess” the natural language of le-

gal texts, isolating meaningful elements in the basic structure of the marked-up document

to reduce the need for computationally-intensive and miserably-unreliable natural-language

40See generally Mark Stamp, Once Upon a Time-Memory Tradeo↵ (2003/07/26),http://www.cs.sjsu.edu/faculty/stamp/RUA/TMTO.pdf (last visited 2013/07/21) (describing and il-lustrating this tradeo↵).

41Compare the space–time tradeo↵ in, for example, data compression: Uncompressed data (like an expan-sive, expressive computational-law structure) takes up the most space but requires no decompression (likerequiring no computational-law interpretation), while increasingly complex and time-consuming algorithmscan help store the same data in less space.

42Cf. Nathaniel Love & Michael Genesereth, Computational Law,http://logic.stanford.edu/people/genesereth/papers/computationallaw.pdf, *1 (2005) (drawing atten-tion to “semantically rich laws, regulations, contract terms, and business rules in the context ofelectronically-mediated actions”).

43See Merriam–Webster (online dictionary), http://www.merriam-webster.com/dictionary/markup%20language (last visited 2013/07/16) (defining “markup language”as, in relevant part, “a system [. . . ] for marking or tagging a document that indicates its logical structure”).

44See Merriam–Webster (online dictionary), http://www.merriam-webster.com/dictionary/xml (lastvisited 2013/07/16) (defining “XML” as “a markup language [. . . ] employing tags that indicate the logicalstructure in addition to the display specifications of the coded data”).

10

processing.45 The “hard problems” inherent in this approach have to do with how to struc-

ture legal data46 so that algorithmic interpretation of the laboriously structured data reduces

to triviality.

The semantic approach to computational law would accomplish something useful: trans-

lating legal texts from something readable only by humans to something readable by ma-

chines.47 This translation promises value by making legal texts “computable”, that is,

amenable to straightforward48 interpretation and processing by computers,49 consequently

reducing the amount of time legal professionals must spend poring over documents, hence

reducing systemic costs.

The semantic approach shows greatest promise in contexts that, by nature, admit of little

ambiguity, relatively simple contexts in which human-readable already approaches machine-

readable. In such contexts, translation from human-readable to machine-readable entails

little more than markup. One apt example concerns contracts about financial options.50

These contracts typically specify a date on which the options expire, and using semantic

markup to signal to computerized readers that a particular date represents the expiration

date for certain options51 could, for example, enable parties to deploy computer programs

45Compare Winchel Vincent, XML and the Legal Foundations for Electronic Commerce, 53 So.Methodist U. L. Rev. 1395, 1404 (2000) (“The power of XML is that it [. . . ] can change data toinformation. XML does this by making it possible for software to capture identifiable pieces of information,place the information into documents, and then ‘parse’ (extract) information from documents automati-cally.”), with Surden, supra note 7, 634 (“[C]ontemporary computer algorithms cannot read or understandeven basic written language texts at anywhere near the sophistication exhibited by a person of ordinaryliteracy.”).

46Cf. Branting, supra note 1, *17 (“It is a very laborious process to take someone’s expertise and formalizeit in a manner that is executable on a computer. So the development costs of legal expert systems are veryhigh and, in my view, they are only going to come down when there are significant improvements in knowledgeacquisition, that is, the process of automating the formalization of expert knowledge.”).

47See, e.g., Vincent, supra note 45, 1406–7 (casting as the central problem the development of a universaldocument format, much like the Microsoft Word “.doc” format, that legal professionals could author andexchange).

48By “straightforward”, I mean to exclude heuristics and other imprecise shortcuts.49See Surden, supra note 7, 635 (“When systems are designed to produce automated assessments of

conformance, we may describe those contract terms as being prima-facie “computable.’ ”).50Surden, supra note 7, 634 n.14 (2012).51Cf. Surden, supra note 7, 649 (“This rigid format—with the label “Option Expiration Date:” followed

by a date on which the option expires—can convey essentially the same information about the expirationdate as the descriptive sentences [. . . ]. By imposing a rigid structure, the parties are simply ensuring that acomputer will be able to read and process [the contract] accurately.”).

11

to monitor contract compliance automatically.52 Whether an expiration date has passed

neither requires nor even admits of human interpretation. When auxiliary systems further-

more provide automatic inputs to the computational-law systems in these contexts, human

intervention can become entirely unnecessary,53 the law truly computing itself.

A semantic approach to computational law that starts by translating legal text into le-

gal data (e.g., legal markup) must first establish the norms that define valid, meaningful

markup.54 In the foregoing example, the two parties to the contract would have to decide

what exactly to call the “expiration date” (e.g., “Option Expiration Date” versus “Expi-

rationDate”) because, generally, computers interpret terms that di↵er slightly as entirely

unrelated.55 One side sending the other side a validly, sensibly marked-up contract but with

computer-readable labels that di↵er from those the recipient expects could result in mis-

communication or malfunction.56 For this reason, the first step in a semantic approach to

computational law must involve laying a groundwork that defines how parties structure the

information they exchange.57

Even then, however, the semantic approach to computational law fails to remove the

eventual need for a synthetic understanding of shades of meanings and how they all interact—

the moment any party characterizes a material piece of information slightly di↵erently from

how the architect of a semantic system anticipated, a brittle system will cease to work as

intended.58 The semantic approach must prepare for parties to encode observations about

52Surden, supra note 7, 635.53Surden, supra note 7, 639–42 (discussing “data-oriented” contracts).54Compare Vincent, supra note 45, 1404 (“A ‘document type definition’ [. . . ] is a set of rules that define

the type, number, and order of elements that may appear in an XML document.”), with Surden, supra note7, 652 (“Data-standards are specifications that provide shared formats for data, and descriptions for howdata should be interpreted, and are capable of expressing standard contracting scenarios.”).

55See, e.g., Vincent, supra note 45, 1405–6 (explaining that computers distinguish among “CourtFiling”,“COURT FILING”, “filing”, and “courtFiling”).

56Cf. Vincent, supra note 45, 1418–9 (explaining the standardization of data formats that LegalXMLproposes to accomplish).

57Cf. Vincent, supra note 45, 1401–2 (explaining the role of “document type definitions” in specifyingwhat will constitute valid XML).

58See Doug Lenat & al., CYC: Using Common Sense Knowledge to Overcome Brittleness and Knowl-edge Acquisition Bottlenecks, The AI Magazine, Vol. 6, No. 4, p. 65 (1985), available athttp://www.aaai.org/ojs/index.php/aimagazine/article/view/510 (last visited 2013/08/03) (“The majorlimitations in building large software have always been [. . . ] its brittleness when confronted by problems that

12

the world either discrepantly or, even if concordantly, still then unexpectedly.59

(a) Computer science has faced analogous problems in a variety of contexts, to which the

field has developed two general responses, one by inferring from unstructured information,

another by deducing from structured knowledge.60 The following paragraphs consider both

of these responses, predicting them both to prove troublesome for computational law.

(i) The first computer-science means of addressing ambiguity—the one exploiting un-

structured information—in a semantic approach to computational law entails resorting to

automated heuristics whereby machines impute meaning to unstructured information. A

system would, for example, search the web for anything that might help the system dis-

ambiguate a disputed term. This strategy resembles implementing strong, general artificial

intelligence.61 General artificial intelligence has proved an intractable project to date, and

this approach to resolving semantic ambiguity in computational law will, in the same mea-

sure, likely prove unfruitful. Computers cannot—now or in the foreseeable future—start

from unstructured information (e.g., the contents of pages on Wikipedia) to arrive at au-

thoritative answers to complicated questions that imply no predetermined answers,62 and all

were not foreseen by its builders[.]”); compare Love & Genesereth, supra note 42, *1 (“[M]achine-processablerules can unambiguously reference the semantically rich and concretely observed facts.”), with Ashley, supranote 1, *3 (“[S]omeone has to represent the facts of the case manually in such a way that the program canknow what the facts are and can determine how to analyze the facts.”) (Machines can accomplish what Loveand Genesereth envision only after some human has completed the nontrivial task, which Ashley describes,of representing facts about the world in a way that machines can process.).

59See Ashley, supra note 3, 804 (“Human legal analysis does not come to a standstill when encounteringa term whose meaning is underspecified, and system designers and knowledge engineers need to model howhumans proceed.”); cf. Branting, supra note 1, *25 (“I think we don’t have a good computer model ofthis process of taking a sort of undirected narrative by someone who isn’t familiar with legal concepts andreformulating it into a fashion that can be manipulated by one of these legal models.”) (implying thathumans play a role in formulating observations).

60Cf. Robert Malone, Structuring Unstructured Data, Forbes (2007/04/04), available athttp://www.forbes.com/2007/04/04/teradata-solution-software-biz-logistics-cx rm 0405data.html (last vis-ited 2013/07/22) (discussing the structured–unstructured dichotomy in the context of a product-warranty claim); see generally Seth Grimes, Structure, Models and Meaning, InformationWeek Soft-ware (2005/03/01), http://www.informationweek.com/software/business-intelligence/structure-models-and-meaning/59301538 (last visited 2013/07/22) (considering unstructured data and structured knowledgein the context of information management).

61See generally Peter Voss, Essentials of General Intelligence, Artificial General Intelligence (BenGoertzel & Cassio Pennachin, eds., 2002), available at http://www.adaptiveai.com/research/index.htm (lastvisited 2013/07/27) (explicating the concept of strong, general artificial intelligence).

62See Cass Sunstein, 8 U. Chi. L. Sch. Roundtable 1, supra note 1, *19 (“The strong version [. . . ]is that artificial intelligence [. . . ] can engage in analogical reasoning[.] [. . . ] [T]he view that [artificial

13

our best minds have yet discerned no clear roadmap to that destination.

IBM’s high-profile Watson project has provided perhaps the most impressive example of a

computer’s performance at reasoning over unstructured information,63 and at least one work

of legal scholarship has suggested that a system like Watson could assist legal professionals in

authoritatively resolving semantic ambiguities.64 Watson consults “a wide range of encyclo-

pedias, dictionaries, thesauri, newswire articles, literary works, and so on”65 to “decompose

[Jeopardy! questions] into . . . parts and [to identify] answers to each [part]”.66 To accomplish

this feat, Watson relies on a committee of more than 100 algorithms that regard the question

from di↵erent angles, proposing a variety of possible answers from which Watson selects the

strongest candidate.67 Watson exemplifies responding to semantic ambiguities by way of

unstructured information.

But Watson’s ability to unravel trivia does not resemble how legal professionals resolve

substantive ambiguities: Whereas Watson responds to Jeopardy! questions based purely on

semantic relationships among words, legal professionals must argue along lines of reasoning

that depend on not only colorable characterizations about the real world but also legal

doctrines and regimes of varying rigidity.68 Watson triangulates correct responses that clue

intelligences] can do it [. . . ] is based on a misunderstanding of what analogical reasoning is[.] [. . . ] Toengage in analogical reasoning [. . . ] [t]here has to be a principle, and at the current state of the art, artificialintelligence can’t generate good principles, or principles at all.”).

63IBM’s Watson has outcompeted the best human players at the television gameshow Jeopardy! byreasoning about the meanings of prompts. See, generally, Dave Ferrucci, How it all began and what’s next,IBM Research News (blog) 2011/12/01 (last visited 2013/07/16) (explaining IBM’s general objectivesand methods for Watson).

64To wit, Cooper, supra note 19, *88 (“This Essay considers whether judges might share the job ofstatutory interpretation with computers like Watson. [. . . ] [The Essay] describes how Watson’s aptitudeslend themselves to textualist-style statutory interpretation [and] discuss[es] how Watson might both aidtextualist interpretation and perhaps perform such interpretation on his own.”).

65David Ferrucci & al., Building Watson: An Overview of the DeepQA Project, AI Mag. (2010/Fall), 69(unavailable at the original source cited in Betsy Cooper, Judges in Jeopardy!: Could IBM’s Watson BeatCourts at Their Own Game?, 121 Yale L.J. Online 87, *89 (2011))

66Id. 6267A Computer Called Watson, IBM, http://www.ibm.com/ibm100/us/en/icons/watson (cited in Cooper,

supra note 19, *89 n.33).68Cf. Sunstein, supra note 1, *20–*21 (“What HYPO can do is come up with cases, and it can be pretty

exhaustive in that, telling how they might be similar and how they might be di↵erent, but in a kind of blindfashion, one that is not alert to the need for a guiding principle that might justify a claim of similarity.HYPO can’t do what needs to be done.”); Branting, supra note 1, *22–*23 (“For example, some of the morenaıve views of legal problem solving are that it is very rule-driven. One of the ways to demonstrate the

14

makers have carefully crafted, but legal professionals must invent persuasive disambiguations

whole cloth—legal professionals simply do not confront the kinds of rigged wordplays that

Watson can answer. Watson could conceivably bring to bear its machine-learning techniques

harvesting unstructured information to support other computational-law systems and/or to

assist humans,69 but no system like Watson can make law work like software.70 As this

paper suggested in an earlier section, the actual work of computational law will require new

high-level algorithms and structures di↵erent from those of Watson for Jeopardy!.

(ii) Computer science has found that replacing unstructured information with structured

knowledge can improve the performance of disambiguation systems and increase their use-

fulness.71 In the context of computational law, this approach would require that human

engineers answer di�cult questions about the meanings of legal concepts and prerecord

those responses so that a machine could incorporate those answers into the overall process

of structured reasoning. This approach obviates the need for imprecise heuristics but, as

a matter of practice, creates something of a fatal recursion: To remove the uncertainties

and ambiguities that the structure-centric, semantic approach to computational law entails,

without resorting to the messiness of machine learning based on unstructured information,

one would introduce additional structures upon structures, building to a vast semantic web.72

Perhaps the most ambitious approach in this vein was Cycorp’s Cyc, a project that

aimed to formalize all of commonsense knowledge in a massive web of concepts linked by

inadequacy of this naıve view is to code up some rules and observe that the resulting system doesn’t reasonanything like a lawyer.”).

69See Sunstein, supra note 1, *26 (explaining how Ashley’s HYPO application could assist judges inidentifying relevant precedent and decisive factors, prompting better reasoned decisions).

70Cf. Cooper, supra note 19, *97 (“Despite these advantages, computers are unlikely to replace judgesanytime soon. For one thing, Watson still makes mistakes at critical times.”).

71See Ashley, supra note 3, *797 (“Ontologies—systematic and explicit specifications of domain conceptsused in legal rules and fact situations—help a computational model to reason flexibly with legal concepts.”).

72See Lenat & al., supra note 58, p. 75 (“Assumption: To build CYC, we must encode all the world’sknowledge down to some level of detail; there is no way to finesse this.”); cf. Ashley, supra note 11, *21–*22 (“For instance, in my example with the argument that CATO generated, it wasn’t just factors butit was reasons why the factors mattered to the legal claim. So I am connecting factors to reasons, andthe arguments are working with those reasons. In work that’s being done by colleagues in Europe, HenryPrakken and Giovanni Sartor, they are representing values, principles that are at stake in cases, and thoseare being worked into the arguments as well. So at least these concepts, these normative concepts, are beingworked into the arguments.”).

15

their semantic relationships.73 The current release of the open-source version of the Cyc

engine (“OpenCyc”) advertises close to 240 thousand “terms” and over 2 million “triples”,74

figures clearly meant to astonish. To the extent of its success, Cyc could have provided

more-authoritative answers than could Watson to the kinds of colorable ambiguities legal

professionals address because, like a legal professional, Cyc reasons rigorously about more

the meaningful relationships among ideas than just the definitions of words.

When one considers, however, that the project’s director hoped to have a working arti-

ficial intelligence by 1993 (after the first 10 years!), a hope that got pushed back to 2001

and still today eludes,75 one sees a certain futility to the project of structuring knowledge to

remove ambiguities from the structure of knowledge. In 1994, one of the project principals

left the team, saying, “CYC is generally viewed as a failed project,” and, “We were killing

ourselves trying to create a pale shadow of what had been promised.”76 Structure-based

responses to semantic ambiguity present two chief problems, namely, how to structure and

represent all the knowledge upon which this response relies and how to analyze that knowl-

edge e�ciently and e↵ectively.77 These intractable problems complement what we considered

in the preceding subsection, the imprecision of tapping unstructured information, together

making post hoc semantic disambiguation an unworkable approach to computational law.

As an alternative to systems for post hoc disambiguation, (b) authoring tools can help

avert ambiguity in the semantic approach to computational law.78 Such tools constrain

users’ choices in constructing legal documents and automatically translate the constrained

choices deterministically into machine-readable markup. In this way, rather than multiply-

73See generally Why Cyc?, Cycorp, http://www.cyc.com/why-cyc (explaining the Cyc project).74OpenCyc, Cycorp, http://www.cyc.com/platform/opencyc (last visited 2013/07/22)75Simson Garfinkel, Happy Birthday, Hal, Wired (date unknown), available at

http://www.wired.com/wired/archive/5.01/↵hal pr.html (last visited 2013/07/22)76Peter Kassan, A.I. Gone Awry: The Futile Quest for Artificial Intelligence, Skeptic, Vol. 12, No.

2 (2005?), available at http://www.skeptic.com/reading room/artificial-intelligence-gone-awry/ (last viewed2013/08/03).

77B.J. Copeland, The CYC Project, What is Artificial Intelligence?,http://www.alanturing.net/turing archive/pages/reference%20articles/what is AI/What%20is%20AI08.html(last visited 2013/08/03).

78Cf. Surden, supra note 7, *654 (discussing “electronic contracting interfaces”).

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ing semantic definitions and references to encompass a greater subset of all information,

authoring tools ensure that users work only within the confines subject to semantic defini-

tion. An authoring tool for a financial contract, for example, might present choices only for

expiration date and price, requiring that the user enter, respectively, a future date and a

positive number, then saving that structured information in a standard format.

Beyond semantic disambiguation and semantic authoring, (c) computational law, as dis-

tinct from computer science, has developed two intermediate means of resolving ambiguities

in the overarching semantic approach.79 The two approaches somewhat reflect the di↵erence

between deterministic and nondeterministic finite-state automata.80

(i) The deterministic means to resolving ambiguity requires users to intervene at (po-

tentially multiple) intermediate states, before the computational-law system arrives deter-

ministically at a single final state. This paper takes the position that this first intermediate

means admittedly facilitates and simplifies computational law but does not itself properly

belong to the field—enabling users to supervise and intervene serves to extend the practical

functionalities of computational-law applications but does not make law work like software.

(ii) A second means, this paper asserts, dominates the first by the measures of both purity

and practicality81: automatically comparing the present facts in their ambiguity to precedent,

automatically identifying strong and weak points of each argument, then presenting all the

alternatives to the user in the final output. This means of resolving ambiguity requires

humans only to adjudicate among the final states, not to supervise all the intermediate

transitions.

(2) By contrast with the semantic approach, the algorithm-centric, syntactic ap-

79Ashley, supra note 3, *805 (citing Donald Waterman & Mark Peterson, Models of Leg. Dec’nmak’g26 (1981)).

80See, e.g., Lydia Sinapova, Deterministic Finite State Automata, Theory of Compu-tation (course materials), http://faculty.simpson.edu/lydia.sinapova/www/cmsc365/LN365 Lewis/L07-FSA.htm (last visited 2013/07/27), and Sinapova, NonDeterministic Finite State Automata, id.,http://faculty.simpson.edu/lydia.sinapova/www/cmsc365/LN365 Lewis/L08-NondetFSA.htm (last visited2013/07/27) (explaining deterministic and nondeterministic finite-state automata).

81Furthermore, researchers have favored this second strategy for addressing ambiguity in the semanticapproach to computational law. Ashley, supra note 3, *805.

17

proach—which inherits from the interdisciplinary field of artificial intelligence and the law—

relies heavily on symbolic manipulation, formal transformation of formal constructs to arrive

at legal conclusions much as one might simplify an algebraic expression.82 The hereinbefore

cited CATO project, for example, works through a rigorous analysis of precedent to construct

arguments about how a court should decide a new case given factors that proved salient in

prior cases.83 Thus CATO addresses itself primarily to the complexities of algorithmic rea-

soning; data structures play an incidental role, but CATO does not focus on the semantic

relationships among ideas, which preoccupies the semantic approach to computational law.

Similarly, Don Waterman’s early computational-law system that advised on whether to set-

tle a products-liability claim posed a series of questions, then applied preprogrammed rules

to the questions’ responses to determine the likeliest outcomes.84

The algorithmic approach to computational law shows most promise in areas of law

that demand complex analysis of facts under regimes of interlocking rules and standards.

The algorithmic approach to computational law does not require that the law operate as

a perfectly logical machine—algorithms engineered for this purpose can accommodate un-

certainty, ambiguity, non-monotonicity, indeterminacy, and defeasibility.85 Causes of action

alleging copyright infringement, for example, require analysis of not only multiple factors

that determine a work’s suitability for copyright but also numerous exceptions and a�r-

mative defenses, an overall tangled mess that lends itself to computation. In transactional

practice, the algorithmic approach to computational law could, for example, help clarify how

provisions in complex contracts interact.

One of the primary practical problems that the algorithmic approach to computational

law must face is incongruity. Although the algorithmic approach to computational law relies

less centrally on specialized data structures representing relationships among ideas than does

82Cf. Branting, supra note 1, *15 (“Computational models based on [mapping legal rules onto computa-tional rules or logical rules] were first developed in the 1970s. They are nothing new. They, in turn, werebased on logical models developed notably by Layman Allen in the 1950s.”).

83See Ashley, supra note 11, *5–6 (explaining how CATO reasons about new cases based on prior cases).84Ashley, supra note 3, 792.85Ashley, supra note 3, 793, 796.

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the semantic approach, the algorithmic approach does require formal representations of legal

rules. Because legal texts fail to provide su�ciently formal representations of legal rules,

the algorithmic approach entails translation of legal rules into formal representations. That

legal rules subsist both informally in legal texts and formally in data structures means that

engineers must ensure both that formal representations of legal rules accurately reflect the

legal texts from which the rules come and that the formal representations continue to reflect

the constantly evolving legal regime.86 When formal representations of legal rules depart

from the corresponding legal texts, we call the situation “incongruity”.

The present paper envisions two kinds of responses to the problem of incon-

gruity: harmonization and unification. We call “harmonization” any e↵orts to ensure

congruity between formal representations of legal rules and the legal texts from which the

rules come.87 We call “unification”, by contrast, an e↵ort to make one and the same docu-

ment both the human-readable legal text and the machine-readable formal representation.

Unification refers to not the markup of legal texts, as in the semantic approach, but the

rewriting of legal texts according to syntactic rules that determine the meaning of the le-

gal texts in the same way that the syntactic rules of a programming language determine

the “meaning” of programs written in that language. The present paper takes the position

that practitioners, engineers, and academics in the field of algorithmic/syntactic computa-

tional law should eventually abandon harmonization for unification. Harmonization might

serve as a bridge from our current legal regimes to a future governed by com-

putational law, but we should aim some day to write legal documents exactly as

we write computer programs today.

86Ashley, supra note 3, 793.87Cf. Ashley, supra note 3, 793 (citing e↵orts by Trevor Bench–Capon and Franz Coenen to design formal

representations of legal rules isomorphic to legal texts because isomorphism simplifies what we have called“harmonization”).

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4 What tools does computational law require?

Regardless of whether we take the semantic/structural approach or the syntactic/algorithmic

approach, we could implement computational-law systems with existing tools subject to

established standards.

In the semantic approach, the project known as LegalXML, for example, has brought

together legal practitioners and technical experts to impress existing standards “to create

standards for the electronic exchange of legal data”.88 LegalXML employs the general-

purpose Extensible Markup Language (XML) in what amounts essentially to a semantic

approach to computational law: structuring information in various legal documents.89 In

the same vein, the Regnet & Regbase Project has represented parts of the Code of Federal

Regulations and of the U.S. Code in XML.90

The syntactic/algorithmic approach could rely on almost any modern programming lan-

guage (e.g., C, Python, Java). Assume for the sake of arguing this assertion that most legal

questions are computable questions,91 that is, assume that we can specify some sequence

of instructions that will answer a given legal question based on our inputs to the system.92

Every computable question is computable by any su�ciently powerful “Turing machine”.93

We call a formal language “Turing complete” i↵ the language is computationally equivalent

88About LegalXML, OASIS LegalXML, http://www.legalxml.org/about/index.shtml (last visited2013/09/06) (“LegalXML is a member section within OASIS[,] the not-for-profit, global consortium thatdrives the development, convergence[,] and adoption of e-business standards. [. . . ] OASIS members partici-pating in LegalXML include lawyers, developers, application vendors, government agencies[,] and membersof academia.”).

89LegalXML: Advancing standards for legal data exchange since 1998, OASIS LegalXML,https://www.oasis-open.org/committees/download.php/12785/OASIS%20LegalXML%20Brochure.pdf(“Mission: The OASIS LegalXML Member Section develops open, non-proprietary technical standards forstructuring legal documents and information using XML and related technologies.”).

90Cf. Repository of Regulations in XML, Regnet, http://eil.stanford.edu/regnet/index.htm (last visited2013/09/06) (linking to examples).

91This assumption we base on the conjecture that no legal questions exists for which we cannot specifysteps—however many and complex—for arriving at the question’s answer.

92Cf. Barker–Plummer, David, Turing Machines, The Stanford Encyclopedia of Philosophy (sum-mer 2013 ed.), http://c2.com/cgi/wiki?TuringComplete (last visited 2013/09/06) (“[A] task is computableif it is possible to specify a sequence of instructions which will result in the completion of the task when theyare carried out by some machine.”).

93Id. § 1 ¶ �1

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to a Turing machine.94 Almost every modern programming language is Turing complete.95

So almost every modern programming language can compute most legal questions, provided

only a proper setup and su�cient time and memory.

But implementing computational-law systems with existing tools subject to established

standards has the potential for unnecessary complexity, circuity, redundancy, and incompat-

ibility. Existing structural and algorithmic tools provide flexibility that exceeds the scope of

computational law, so, to speak in simile, using such existing tools as XML or C to approach

computational law seems like writing custom software, complete with a graphical user inter-

face, to run on a supercomputer for the simple purpose of adding single digits. Any software

engineer who has ever from scratch coded a graphical simulation of an adding machine will

understand why this approach practically guarantees a series of intermediate problems and

headaches together disproportionate to the benefits of the final product.96

The present paper takes the position that we should expect to encounter just such a

cost–benefit imbalance when attempting to build computational-law systems using general-

purpose computing standards. Applying general-purpose computing standards to legal do-

mains leaves too many di�cult questions unanswered, too many options open to each partic-

ular system’s engineers. On the one hand, the cost of developing each new computational-law

system presents a considerable barrier to entry; on the other hand, no system’s engineers can

rely on recuperating the cost through networking e↵ects with other systems because general-

purpose computing standards provide no guarantee that two computational-law systems will

work together. Thus suboptimal suitability reduces the net benefits of computational law,

creating relative disincentives to pursue research and development in this field.97

94Brian Raiter, Turing Comptele, Cunningham & Cunningham, Inc.,http://c2.com/cgi/wiki?TuringComplete (last visited 2013/09/06).

95Id.96Cf. Marjan Mernik & al., When and How to Develop Domain-Specific Languages, 37-4 ACM Comput-

ing Surveys 316 (2005/12), 316 (“[Domain-specific languages (DSLs) are languages tailored to a specificapplication domain. They o↵er substantial gains in expressiveness and ease of use compared with general-purpose programming languages in their domain of application.”).

97Cf. id. at 317 (“DSLs trade generality for expressiveness in a limited domain. By providing notationsand constructs tailored toward a particular application domain, they o↵er substantial gains in expressivenessand ease of use compared with GPLs for the domain in question, with corresponding gains in productivity

21

Apparent need not withstanding, the nascent field of computational law lacks a robust

repertoire of domain-specific languages, perhaps because, on the one hand, few legal profes-

sionals have attained su�cient proficiency in software engineering and, on the other hand, few

software engineers possess su�cient background in reading and interpreting legal text.98 The

present paper proposes that computational law—law that works like software—

needs language systems99 designed and standardized specifically for legal do-

mains. Any such language system we should build to realize the following desiderata: (1)

independence, (2) correctness, (3) expressiveness, (4) determinacy, and (5) (a) succinctness

and (b) usability.

1. We will call a language system “independent” i↵ the system requires no additional

language system(s) to practice computational law. LegalXML and Regnet & Regbase,

for example, both fail the test of independence because these systems require the

addition of interpreters that LegalXML and Regnet & Regbase, by themselves, fail to

provide or even specify. By contrast, the C programming language, a Turing-complete

language, would meet the test of independence because, hypothetically, we could create

a fully-functional computational-law system exclusively in C.

2. We will call a language system “correct” i↵ the responses it computes from given

inputs reflect the responses at which legal professionals would arrive given identical

inputs. Remark that “correct” need not imply “unambiguous”—we can separate the

two either by enabling computational-law system to preserve and report ambiguity or

by restricting our programming language’s domain to the subset of legal questions that

give rise to no ambiguity (but see the following desideratum).

3. We will call a language system “expressive” to the extent that we can translate ordinary

and reduced maintenance costs.”).98Cf. id. at 316 (“DSL development is hard, requiring both domain knowledge and language development

expertise. Few people have both. Not surprisingly, the decision to develop a DSL is often postponedindefinitely, if considered at all, and most DSLs never get beyond the application library stage.”).

99The present paper uses “language system” informally to mean the entirety of denotation, interpretation,compilation (if any), and operation, as with, for example, the C language and the GNU C Compiler (GCC).

22

legal documents into the language system without sacrificing correctness, such that

inputs to our translation produce the outputs we would expect to get were we manually

to consider the same inputs beneath the ordinary legal documents. A language system

we will call “unexpressive” to the extent that ordinary legal writing permits us to draft

provisions that we cannot correctly translate into our language system.

4. We will call our new language “determinate” i↵ a given program always generates the

same responses to the same inputs. Determinacy follows our intuitive understanding

of correctness of legal analysis: We presume that a legal professional could not rightly

draw di↵erent conclusions at di↵erent times given identical information. Provided

the substantive law has not changed, how we reason about unchanged law should

not change. As with “correctness”, determinacy does not preclude ambiguity; rather,

provided the substantive law has not changed, whatever ambiguities we do see should

not change. Readers familiar with the di�culties of parallel computing will understand

the importance of insisting on determinacy when our language system will have to

dispense with the convenience of strict sequentiality—all laws pertain to everything

all the time, without temporal prioritization or ordinality. Regardless of the order in

which legal provisions come to the attention of our computational-law language system,

the result must remain unchanged for given inputs.

5. Finally, we will call our new language (a) “succinct” and (b) “usable” i↵ legal professionals—

not necessarily geniuses of software engineering—can represent legal texts and answer

legal questions through use of (a) short programs (b) without di�culty, either men-

tal/conceptual or technical, substantially more than the di�culty that such legal pro-

fessionals face in the traditional practice of law.100

To realize these desiderata, computational law needs its own standard language

system, some “Claw” to serve computational law as C has served computer

100Cf. Mernik & al., supra note 96, 317 (“[B]y reducing the amount of domain and programming expertiseneeded, DSLs open up their application domain to a larger group of software developers compared to GPLs.”).

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science.

5 Where is computational law going?

Computational law shows all the signs of proving itself the legal industry’s poster child of

“disruptive innovation”.101 Computational law has not yet widely penetrated the market, but

with so much money at stake102 and such aversion of established providers to the faster and

the cheaper,103 disruption (i.e., by newcomers starting from the bottom and growing upward)

appears only a matter of time. Relentless change in the technological and informational

structure of society makes disruption still more certain, for practitioners who fail to take

advantage of law that works like software will find themselves unable to cope with the

volumes of information our society produces.104 Advantages will flow to those who come

early to this emerging field.

Law working like software should prove not a contingency but an eventuality. When

exactly we arrive at that eventuality depends upon neither white-shoe nostalgia nor sci-fi

mania, on not what people believe about the law but whether clever engineers can design and

implement systems. Computational law will likely begin with specialized systems

but should progress to more-general systems. The touchstone against which

we can measure our progress is the hypothetical Codex Machine. Meanwhile,

computational law will likely begin with harmonization of systems to texts but

101See generally Clayton Christensen, The Innovator’s Dilemma: When New Technologies CauseGreat Firms to Fail (1997) (coining “disruptive innovation” to describe “a process by which a prod-uct or service takes root initially in simple applications at the bottom of a market and then relentlesslymoves up market, eventually displacing established competitors”, Clayton Christensen, Disruptive Innova-tion, http://www.claytonchristensen.com/key-concepts/ (last visited 2013/08/01)).102See Joshua Kubicki, 6 Reasons the Legal Industry is Ripe for Startup Invasion, Tech Cocktail

2013/03/25, http://tech.co/legal-industry-startup-invasion-2013-03 (last visited 2013/08/01) (calling theU.S. legal industry “roughly a $300 billion market”).103Cf. Cari Sommer, How Entrepreneurship is Reshaping the Legal Industry, Forbes 2013/07/24,

http://www.forbes.com/sites/carisommer/2013/07/24/how-entrepreneurship-is-reshaping-the-legal-industry/ (last visited 2013/08/01) (“[L]aw is typically a billable hour profession, and less time equals lessmoney.”).104See generally George Paul & Jason Baron, Information Inflation: Can the Legal System Adapt?, 13

Rch. J.L. & Tech. 10 (2007) (arguing that the legal profession must make better use of technology tostay abreast of today’s surging flood of information).

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could end with the complete unification of systems and texts, toward the goal

that humans and computers prove equally able at reading and interpreting legal

systems/texts, arriving at identical conclusions.

Accordingly, this paper calls for a system-centric approach to computational law—we

possess already all the theory we need, so our research and development should focus on

specific systems (whether abstract or concrete), laying out either technically how to imple-

ment a certain computational-law system or precisely wherein some such system falls short.

This paper’s claims admit of proof less by argument than by implementation, of disproof

primarily by failure to deliver.

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