Hunter, Daniel; Tyree, Alan; Zeleznikow, John --- "There is Less to this Argument than Meets the Eye" [1993] JlLawInfoSci 5; (1993) 4(1) Journal of Law, Information and Science 46
[*] Daniel Hunter lectures in information technology law and artificial intelligence and law at the Law School, University of Melbourne. He has published a number of articles on the law of computers, and with Dr Zeleznikow has previously written on Artificial Intelligence and Law. He is co-editor of Computers & Law, the journal of the Australian and New Zealand Societies for Computers and Law. He is a Barrister and Solicitor of the Supreme Court of Victoria and High Court of Australia, and has worked as a computer programmer and in practice as a solicitor.
[*] Alan Tyree is the Landerer Professor of Information Technology and Law at the University of Sydney. He is a co-founder with Graham Greenleaf and Andrew Mowbray of the DataLex Project which has conducted research into legal AI systems and advanced database design. He has recently received a National Teaching Development Grant from the Committee for the Advancement of University Teaching to implement a cost-effective form of computer assisted learning for a number of law subjects.
[*] Dr John Zeleznikow is the Head of the Legal Reasoning Group and a Senior Lecturer in the Department of Computer Science and Computer Engineering, La Trobe University, Melbourne. His current area of research involves integrating rule based and case based reasoning to legal expert systems. He has published numerous articles on the construction of advanced legal expert systems, and has taught Artificial Intelligence and Law at the Law School, University of Melbourne.
[1] Moles, R.N. ‘Logic Programming - An Assessment of its Potential for Artificial Intelligence Applications in Law’, (1991) Volume 2(2) Journal of Law and Information Science 137-164 ; generating the response, Zeleznikow, J. and Hunter, D. ‘Rationales for the Continued Development of Legal Expert Systems’, (1992 ) Vol 3 (1) Journal of Law and Information Science 94-110 and Tyree, A., ‘The Logic Programming Debate’, (1992 ) Vol 3 (1) Journal of Law and Information Science 111-116; both of which provided the impetus behind Moles, R.N. and Dayal, S. ‘There is more to life than logic’, (1992) Vol 3(2) Journal of Law and Information Science p188-218
[2] Moles,R.N., op.cit (hereafter ‘Moles’). There was also some comment of a similar nature in the report on the major AI and Law conference contained in Brown, D., ‘The Third International Conference on Artificial Intelligence and the Law’, (1991) Volume 2(2) Journal of Law and Information Science p233-239
[3] Sergot, M.J., Sadri, F., Kowalski, R.A., Kriwaczek, F., Hammond, P. and Cory, H.T., ‘The British Nationality Act as a Logic Program’,(1986)Volume 29(5) Communications of the ACM 370-386
[4] Zeleznikow, J, & Hunter, D., op.cit. (hereafter ‘ZH’)
[5] Tyree, op.cit. (hereafter ‘Tyree’)
[6] For an overview of these researches see ZH, p107-110
[7] Moles, R.N. and Dayal, S. ‘There is more to life than logic’, (1992) Vol 3(2) Journal of Law and Information Science p188-288 (hereafter ‘MD’)
[8] For those who worry that the litigation analogy is not only far from perfect but also dangerously divisive (see for example MD, p189), we would just comment there is a conflict between our opposing viewpoints. Indeed, MD refers to narrowing the issues, ‘as with the lawyers’ exchange of pleadings’ (MD, p189). Happily for us all, no adjudicator sits in judgment of us. The old aphorism seems to hold true here as elsewhere: ‘The reason why battles between academics are so hard-fought is because there is so little at stake.’
[9] MD, p190
[10] Also known as Natural Language Processing (NLP).
[11] MD, p191
[12] See for, example, Winston, P.H., Artificial Intelligence, Addison Wesley, Third Edition, 1992.
[13] MD at 191 quoting ZH at 97
[14] Part of the work of researchers involved in NLP.
[15] Seen before in the earlier Moles article.
[16] By this we mean those poor unfortunates in practice as barristers and solicitors who do not have the benefit of a deep jurisprudential understanding, and instead must interpret the law as they find it. It is the experience of one of the authors and all of the practitioners surveyed that jurisprudence plays little part in day-to-day legal practice. Whether this is good or bad is irrelevant—it is merely true.
[17] Which the lawyers extracted from the cases itself.
[18] ZH, p101
[19] Polya, G, Patterns of Plausible Inference—a guide to the art of plausible reasoning, (Princeton University Press, 1954).
[20] See the earlier Moles article.
[21] Or for that matter, statutory regulation, common law doctrine, leading case, etc etc. The difference is immaterial since all in some way are ‘law’ to which the lawyer must have access and upon which the lawyer must advise.
[22] ZH, p96
[23] MD, p191
[24] Witness the ongoing and unresolvable debates between the positivists, rule-sceptics and the various other jurisprudential schools, who argue over how many lawyers can dance on the head of a pin.
[25] Again a point one of us has raised before: Tyree p115. See also Berman, D.H. and Hafner, C. ‘The potential of artificial intelligence to help solve the crisis in our legal system’, (1989) vol 32(8) Communications of the ACM 928-938. Strangely, MD raises the question of access to justice as question in determining the outcome of cases (MD, p199) and yet fails to recognise the potential benefits that LESs possess.
[26] [1932] AC 562
[27] This representation is not ruled out by problems with what is called ‘np-completeness’ which plague other areas of human endeavour.
[28] See for example HYPO (Ashley, K. D., ‘Arguing by Analogy in Law: A Case Based Model, (1988) Analogical Reasoning, pp 205-224 and Ashley, K. D., Modelling Legal Argument- Reasoning with Cases and Hypotheticals, (Cambridge, MA: Bradford/MIT Press, 1990); CABARET (Rissland, E.L. and Skalak, D.B. ‘CABARET: Rule Interpretation in a Hybrid Architecture’ International Journal of Man Machine Studies, 34(6) 1991, pp. 839); GREBE (Branting, L.K., ‘Building Explainations from rules and structured cases’, International Journal of Man Machine Studies, 34(6) 1991, pp 797 - 838 ); FINDER (Tyree, A. L., Greenleaf, G. and Mowbray, A., ‘Legal reasoning: the problem of precedent’, Proc. Conf. AJAI, Sydney, November 1987, pp 419-432); PROLEXS (Walker,R.F., Oskamp,A., Schrickx,J.A., Opdorp,G.J., Berg,P.H. van den , ‘PROLEXS: Creating Law and Order in a Hetorogeneous Domain’, International Journal of Man Machine Studies 35(1) 1991, pp. 35-68); IKBALS (Vossos, G., Zeleznikow, J., Dillon, T. (1990c), ‘Combining Analogical and Deductive Reasoning in Legal Knowledge Base systems - IKBALS II’, in Legal Knowledge Based Systems- Aims for Research and Development, (Koninklijke-Vermande 1991) pp 97-105.
[29] Hence the research into the use of standard AI architecture in law domains. Such architectures include frames, schemas, lists, logics, and a range of newer AI architectures such as object orientation, distributed AI systems and blackboard systems. These are simply tools to make representation and operation more efficient.
[30] An issue which we have dealt with before in the discussion of the proof that production rules models are equivalent to Turing Machines, and hence if computers can eventually reason as lawyers there is no reason why the computer could not be a production rule system. See Tyree p 113.
[31] MD also takes the opportunity of misrepresenting one of the seminal researchers in the application of jusrisprudence to AI and Law. It would be remiss of us not to mention this misrepresentation, for it gives a false impression of the work of Richard Susskind. MD says that the researchers in AI and Law are modelling consensus, and that this modelling of consensus ‘…is a central aspect of the work of Richard Susskind Expert Systems in Law (1987) Oxford University Press. The whole of his approach is based on the search for consensus.’ MD p195, n29. While this is true, the consensus of which Susskind speaks is not consensus in the legal source or legal representation, but rather consensus in jurisprudence. Susskind writes, ‘It would indeed be embarrassing for all concerned with jurisprudence if it transpired that we had to admit to computer scienists that, though we have been speculating about the nature of law and legal reasoning for well in excess of two thousand years, no matters of controversy have been settled, no agreement attained, in consequence of which legal theory has little to offer to the development of techniques of legal knowledge engineering. Such an admission is, of course, not necessary if legal theorists confine the bulk of their attention, as I do in this book, to identifying consensus within jurisprudence.’ Susskind op.cit. p35 It seems that the authors of MD claim that there is no consensus at all in jurisprudence, an assertion which Susskind disproves.
[32] MD p196-197
[33] Interestingly, the Hartian doctrine claims that law has a core with an umbra surrounding it. This accords fairly satisfactorily with most practical applications, though clearly would be rejected by Moles.
[34] Note the experience with MYCIN, Shortliffe, E.H., Computer Based Medical Consultations: MYCIN, New York, Elsevier, 1976.
[35] MD p197
[36] We skip MD’s discussion under the heading ‘ “Rules” “Categories” and “Consensus” as constructs.’ This section of the paper simply applies the principles explained earlier in the paper, which we have mentioned and answered. The primary thesis of this section appears to be that the authors of MD can always find an argument for any client, in the face of any precedent of lack of it. The reductio ad absurdum of this form of reasoning is that Bob Moles can win every case he takes. The discussion in any event ignores the fact that Ashley’s HYPO (Ashley, op.cit.) amongst others has sought to emulate the lawyer’s technique of arguing from both sides of a given case.
[37] MD p205
[38] MD p205, n.56
[39] MD p219
[40] ZH, p96-99, Tyree, p113-4
[41] ZH, p 103
[42] MD, p189
[43] To name but a few examples, there has been a great deal of discussion on the following jurisprudential questions in the standard AI and Law literature:
Toulmin’s structure of argument, see Lutomski, ‘The design of an attorney's statistical consultant’, Proceedings of the Second International Conference on Artificial Intelligence and Law, (ACM Press 1989); Marshall, ‘Representing the Structure of Legal Argument’, Proceedings of the Second International Conference on Artificial Intelligence and Law, (ACM Press 1989); Dick ‘Representation of Legal Text for conceptual retrieval’ Proceedings of the Third International Conference on Artificial Intelligence and Law, (ACM Press 1991); Bench-Capon, Lowes and McEnery, ‘Argument based explanation of logic programs’ (1991 ) Vol4 No 3 Knowledge Based Systems .
Arguing both sides of a case, seeAshley, K., op.cit; Rissland, E.L. and Skalak, D.B., op.cit.
Jurisprudence, AI and Law generally, see Susskind, op.cit.
[44] In First Order Predicate Calculus, one common type of logic system.
[45] ZH, p97-103
[46] For a detailed description of logic in law, see Zeleznikow & Hunter, Building Intelligent Computer Aided Legal Information Systems: Destroying the Myths, chapter 6, accepted for publication, Kluwer Law & Taxation Publishers.
[47] It may be simplistic to say that determining whether someone was drinking is a mere fact. In Victoria, Australia, anyone who has a blood alcohol level of above .05% is deemed to be drinking. So we might write this as a rule blood_alcohol_level(x,y) & (y > .05) -> drinking(x). But even then a lawyer could argue that blood_alcohol_level is not automatically verifiable, because the machinery measuring blood alcohol level might be faulty. Nevertheless, we shall assume, for simplicity, that blood alcohol testing machines are accurate and give a legally undebatable ruling as to whether a person has been drinking under the Victorian Motor Traffic Act.
[48] This is a very strange charge to lay against two mathematicians and a computer scientist! One wonders what kind of mathematics, if any, is taught to potential lawyers.
[49] Or are reduced to the status of religion or superstition.
[50] See MD, p197-204.
[51] Or rather, what the authors of MD suppose that those systems do.
[52] MD, p199.
[53] This type of reasoning is an essential feature of the machines that Mead and Johnson have built.
[54] MD, p200.
[55] That is: Does it ‘work in practice’?
[56] Tyree p 114, quoted in MD p189.
[57] MD, p190.