The spring of the AI and Law Community

Beginning on the second half of the Seventies and then throughout the Eighties, that of AI and Law establishes itself as a community united by a set of shared methodologies and ambitions⁵⁵⁴. Between the many possible “formalizations” of the research program of such community, that expressed by Edwina Rissland in 1990 identifies as the goals of AI and Law research:

1. Reason with cases (both real and hypothetical) and analogies; 2. Reason with rules;

3. Combine several modes of reasoning; 4. Handle ill-defined and open-textured concepts; 5. Formulate arguments and explanations; 6. Handle exceptions to and

550 Ivi

551 Ivi, p. 45

552 Ivi, p. 46. As the Authors add, it is likely that the lawyers will be frustrated by “the gap between what they want to say and what the computer language lets them say”

553 As the research that McCarthy conduced on TAXMAN as of 1972, the program JUDITH, and the approach adopted in the project LEGOL by the team led by Ronald Stamper at the London School of Economics. See L. Thorne McCarty, Reflections on "Taxman": An Experiment in Artificial Intelligence and Legal Reasoning, in Harvard Law Review, 1977, 90, 5, pp. 837-893; Id., The TAXMAN Project: Towards a Cognitive Theory of Legal Argument, in Bryan Niblett (ed.), Computer Science and Law: An Advanced Course, Cambridge University Press, Cambridge, 1980, p. 23; Walter G. Popp, Bernhard Schlink, JUDITH, A Computer Program to Advise Lawyers in Reasoning a Case, in Jurimetrics, 1975, 15, p. 303; Ronald Stamper, LEGOL: Modelling legal rules by computer, in Bryan Niblett (ed) Computer Science and Law, Cambridge University Press, Cambridge, 1980, p. 45

554 The first international meeting was organized in 1979 in Swansea by Bryan Niblett, see Bryan Niblett (ed.), Computer Science and Law: An Advanced Course, cit.. In 1981 the First International Conference on Informatica, Logica, Diritto was organized in Florence and, in 1987, the first International Conference on Artificial Intelligence and Law (ICAIL) was held in Boston.

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conflicts among items of knowledge, like rules; 7. Accommodate changes in the base of legal knowledge, particularly legal concepts, and handle non-monotonicity, that is, changes in which previous truths no longer hold as more becomes known; 8. Model common sense knowledge; 9. Model knowledge of intent and belief; 10. Perform some aspects of natural language understanding⁵⁵⁵

Contemporaneously, Thorne McCarty distinguished the two main motivations driving the first phase of AI and Law research: on one hand, a practical motivation, that of building "intelligent legal information systems that can assist both lawyers and nonlawyers in their interactions with both legal and nonlegal rules”; on the other hand, a theoretical motivation, that of “trying to gain a better understanding of the process of legal reasoning and legal argumentation, using computational models and techniques”⁵⁵⁶.

Even if, in practice, the two motivations often go hand in hand – a better understanding of legal reasoning benefits the design of tools directed to practitioners, and vice versa – it is worth highlighting that, while the cognitive approach was receding in AI, AI and Law was largely interested in using computers to explain the actual functioning of human legal reasoning. Whether, on one hand, this may be interpreted as a feature descending from the focus, in the legal realm, on issues of legitimation and validity - we want computers to decide as humans do according to law, on the other it can be seen as the acknowledgment that an explanation of law could not be provided only by looking at “the theory”, but only by opening the human black box. In this sense, a comment made by Danièle Bourcier, who encourages the study of “the judge’s reasoning (rather than judicial reasoning)”, is particularly telling⁵⁵⁷.

The constant confrontation with the epistemological plausibility, the concrete computational viability and the legal relevance of the specific entanglement of the relation law-jurist-machine advanced by the AI and Law community is reflected in the way in which knowledge representation and reasoning models are articulated throughout the first decades of research.

The first of the knowledge representation challenges faced by the AI and Law community was one which is peculiar of the legal field, that is how to represent legal rules and the relations between them. The assumption is clearly presented by Jon Bing:

There can be a dependency between causal and legal relations. An observer can find a causal relation between a criminal act and custodial sentence, but behind this causal relation there will be a normative relation since it is a legal norm that establishes that

555 Edwina L. Rissland, Artificial Intelligence and Law: Stepping Stones to a Model of Legal Reasoning, in The Yale Law Journal, 1990, 99, p. 1963

556 Thorne L. McCarty, Artificial Intelligence and Law: How to Get There from Here, in Ratio Juris, 1990, 3, 2, p. 189

557 Danièle Bourcier, The Judge’s Discourse: Research on the Modelization of Reasoning in Law, in Costantino Ciampi (ed.), Artificial Intelligence and Legal Information Systems, cit., p. 106

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the criminal act has to be classified as a “S” to which the custodial sentence “C” is connected.

It is easy to see that a normative relation can be represented as a causal relation (or rather probabilistic). Currently, this is what it is assumed in representing a legal norm in a computer: the normative relation established by the legal norm is represented as a causal relation governed by the program of the computer⁵⁵⁸

As with Turing for machine intelligence, such a translation of norms into causal mechanisms defines the great power as much as the limitations of machine legal intelligence.

The implementation of the first Legal Information Systems⁵⁵⁹ is distinguished by an approach oriented towards the modelling of programs that, as Prakken and Sartor highlight, reason with the law, which is represented as an axiomatic system⁵⁶⁰. Legal knowledge is represented as a concatenation of rules tied by relations of implication, while statutory reasoning is carried out through logical deduction⁵⁶¹. The Eighties are marked by a considerable interest in developing such paradigm. The representation of law as executable logic program is experimented in particular by the Logic Programming Group at the Imperial College, that codified the British Nationality Act 1981⁵⁶² and the Supplementary Benefit Legislation⁵⁶³.

558 Jon Bing, Sistemi deontici: un tentativo di introduzione, in Antonio A. Martino, Enrico Maretti, Costantino Ciampi (eds.), Logica, Informatica e Diritto, Le Monnier, Firenze, 1978, p. 123, my translation

559 Costantino Ciampi (ed.), Artificial Intelligence and Legal Information Systems, North-Holland, Amsterdam, 1982; Antonio A. Martino, Deontic Logic, Computational Linguistics and Legal Information Systems, North-Holland, Amsterdam, 1982

560 Henry Prakken, Giovanni Sartor, Law and Logic: a Review of an Argumentation Perspective, in Artificial Intelligence and Law, 2015, 227, p. 217

561 Kevin. D. Ashley, Artificial Intelligence and Legal Analytics: New Tools for Law Practice in the Digital Age, Cambridge University Press, Cambridge, 2017, p. 38

562 Marek J. Sergot, Fariba Sadri, Robert A. Kowalski, Frank Kriwaczek, Peter Hammond, H. T.

Cory, The British Nationality Act as a Logic Program, in Communications of the ACM, 1986, 29, 5, p. 370

563 Trevor J.M. Bench-Capon, Gwen O. Robinson, Tom W. Routen, Marek J. Sergot, Logic programming for large scale applications in law: A formalisation of Supplementary Benefit Legislation, in Proceedings of the First International Conference on Artificial Intelligence and Law, ACM Press, New York, 1987, pp. 190. Since the first attempts to design operative computational models of legal systems, the degree of isomorphism required for the representation of statutes have been object of debate. See, for instance, the attempt to develop a formalized representation of the Argentinian ley 9688 concerning injuries on workplace in Ricardo A. Guibourg, Formalizzazione del ragionamento in materia di infortuni sul lavoro, in Antonio A. Martino, Enrico Maretti, Costantino Ciampi (eds.), Logica, Informatica e Diritto, Le Monnier, Firenze, 1978, p. 244. Beside Sergot’s approach, based on resemblance, Jurgen Karpf developed the concept of isomorphic representation and defined it as the representation in which “(i) Each legal source is represented separately. (ii) The representation preserves the structure of each legal source. (iii) The representation preserves the traditional mutual relations, references and connections between the legal sources. (iv) The representation of the legal sources and their mutual relations.., is separate from all other parts of the model, notably representation of queries and facts management. (v) If procedural law is part of the domain of the model then the law module will have representation of material as well as procedural rules and it is demanded that the whole system functions in accordance with and in the order following the procedural rules”. See, Jurgen Karpf, Quality assurance of legal expert systems, in

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In parallel to the attempts directed towards specific bodies of law, the AI and Law community elaborated an ontology of the legal relations, from the characterization of deontic modalities⁵⁶⁴, to the rights related to other’s obligations, permissive rights, erga-omnes rights, normative conditionals, liability rights, different kinds of legal powers, potestative rights (rights to produce legal results), result-declarations (acts intended to produce legal determinations), and sources of the law⁵⁶⁵.

Referring in particular to analogical reasoning, already in 1981 Leo Reisinger affirmed that “The application of AI to legal systems is hindered by the fact that some of the most important elements of legal decision-making have so far not been satisfactorily described by means of formal logic”⁵⁶⁶. In a way, those which were seen as the inherent limits of the rule-based approach⁵⁶⁷ were taken by the AI and Law community as the evidence motivating the exploration of different computational formalisms capable of expressing “what jurists do” when they reason about the law and the relations between law and facts.

The need to both enlarge the knowledge base to include those rules that are not expressed into statutes, and develop more complex models for expressing the process of legal reasoning is clearly articulated by Anne Gardner in PhD dissertation, published in 1987 as An Artificial Intelligence Approach to Legal Reasoning⁵⁶⁸ and in the research conducted by Edwina Rissland, Kevin Ashley and David Skalak. What these researchers proposed was to address case-based reasoning by developing a formalism capable of complementing those forms of rule-based processes used in statutory reasoning with models capable of both, representing facts, principles and values, and reason with them through analogy.

On the basis of the advances in both rule-based and case-based systems, throughout Nineties AI and Law researchers headed towards the development of new forms of

Antonio A. Martino (ed.), Pre-Proceedings of the Third International Conference on Logica, Informatica, Diritto, Volume I, CNR, Florence, 1989, p. 411. The notion of isomorphism was then elaborated and made popular especially thanks to the work of Trevor Bench-Capon. See Trevor J. M.

Bench-Capon, Deep Models, Normative Reasoning and Legal Expert Systems. In Proceedings of the Second international conference on Artificial Intelligence and Law, ACM Press, New York, 1989, p.

37–45; Trevor J. M. Bench-Capon, Frans P. Coenen, Isomorphism and legal knowledge based systems, in Artificial Intelligence and Law, 1992, 1, p. 65

564 Leyman E. Allen, Charles S. Saxon, Analysis of the logical structure of legal rules by a modernized and formalized version of Hohfeld fundamental legal conceptions, in Antonio A. Martino, Fiorenza Socci Natali (eds.), Automated Analysis of Legal Texts, North-Holland, Amsterdam, 1986, p.

385.

565 Giovanni Sartor, Fundamental Legal Concepts: A Formal and Teleological Characterisation, in Artificial Intelligence and Law, 2006, 14, 1-2, p. 101

566 Leo Reisinger, Legal Reasoning by Analogy. A Model Applying Fuzzy Set Theory, in Costantino Ciampi (ed.), Artificial Intelligence and Legal Information Systems, cit., p. 151;

567 Philip Leith, Clear rules and legal expert systems, in Antonio A. Martino, Fiorenza Socci Natali (eds.), Automated Analysis of Legal Texts, North-Holland, Amsterdam, 1986, p. 661

568 Anne von der Lieth Gardner, An Artificial Intelligence Approach to Legal Reasoning, The MIT Press, 1987

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logics capable of expressing the non-monotonic dialectic that distinguishes legal argumentation⁵⁶⁹.

In any case, both when intended narrowly, as in rule-based system, or more broadly, as in case-based system and computational models of legal arguments, legal rules are necessarily understood as formal procedures akin to a calculus.