The Inadequacy of Legal Rules and the Rule of Data

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possibility, offered by machine learning and deep learning, to automate the formalization of rules.

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Niblett identify a limit of computational machines in the fact that judges do “more than simply apply rules or standards”⁶³⁵. As a result of the adoption of a mechanist conception of rule following, however, such quid pluris must consist in an implicit process-mechanism which govern the behaviour of rule followers without the latter being able to access and formulate it. In this light, the endorsement of the anti-formalist critique of legal rules does not lead to the rejection of formalism. On the contrary, the Rule of Machines perspective takes very seriously the ideal of a form of government based on a complete system of rules. Once again, the solution to overcome the aporias of legal formalism⁶³⁶ depends on the reframing of the reasons

Hans Kelsen, Pure Theory of Law, cit., p. 349. Thirdly, Hart contrasted the dogma of completeness of the legal system by considering the latter as distinguished by an inevitable open-texture character.

Discussing Jhering’s criticism against the Begriffjurisprudenz, Hart refers to the “porosity of concept”

(porositat der Begriffe) to argue that “We have no way of framing rules of language which are ready for all imaginable possibilities. However complex our definitions may be, we cannot render them so precise so that they are delimited in all possible directions and so that for any given case we can say definitely that the concept either does or does not apply to it”⁶³⁴. While in the Essays it is presented in terms of linguistic precision, in The Concept of Law, the British philosopher configures the problem in terms of anthropological limitations: “men who make laws are men, not gods. It is a feature of the human predicament, not only of the legislator but of anyone who attempts to regulate some sphere of conduct by means of general rules, that he labours under one supreme handicap - the impossibility of foreseeing all possible combinations of circumstances that the future may bring. A god might foresee all this; but no man, not even a lawyer, can do so”, Id., The Concept of Law, cit., pp. 269-270. Lastly, the theme of rule-scepticism and legal indeterminacy has been subject to extensive discussion in relation to Wittgenstein’s remarks on rule following, Saul A. Kripke, Wittgenstein on Rules and Private Language, Blackwell, Oxford, 1982; Charles M. Yablon, Law and Metaphysics, in The Yale Law Journal, 1987, 96, 25; Margareth Radin, Reconsidering the Rule of Law, cit.; Brian Bix, The Application and MisApplication of Wittgenstein's Rule Following Considerations to Legal Theory, in Canadian Journal of Law and Jurisprudence, 1990, 3, 2, p. 107; Andrei Marmor, No Easy Cases?, in Canadian Journal of Law and Jurisprudence, 1990, 3, 2, p. 61^; Christian Zapf, Eben Moglen, Linguistic Indeterminacy and the Rule of Law: On the Perils of Misunderstanding Wittgenstein, in The Georgetown Law Journal, 1996, 84, p. 485; Dennis Patterson, Wittgenstein on Understanding and Interpretation (Comments on the Work of Thomas Morawetz), in Philosophical Investigations, 2006, 29, 2, p. 129. Many contributions are collected into two volumes: Dennis M. Patterson (ed.), Wittgenstein and legal theory, Westview Press, Boulder, 1992; Id. (ed.), Wittgenstein and Law, Routledge, London, 2004.

635 Anthony Casey, Anthony Niblett, The Death of Rules and Standards, cit., pp. 1433-34; 1445. The Authors identify as a field in which legal decisions seem to require “something more” than mere rule-application in the assessment of constitutional legitimacy of statutes. In their view, this would explain the difficulty which machines might encounter in making such decisions. Cfr, also, Anthony Casey, Anthony Niblett, Self-Driving Laws, cit., p. 436; Daniel Goldsworthy, Dworkin’s dream: Towards a singularity of law, in Alternative Law Journal, 2019, 44, 4, p. 289

636 As discussed above, the ideal of completeness inspired the supporters of Codification and the understanding of legal system advanced by the nineteenth century German legal science. On one hand, the achievement of completeness was sought by attempting to formalize rules with such a degree of explicitness as to fill any possible gap. On the other, the methodological approach that jurists were to adopt - literal interpretation, logical deduction – was held to be capable of ensuring that legal propositions conveyed their meaning in an undisputable way. The capacity to regulate any possible case was considered an attribute of the system itself, the jurist only had to passively follow the path set out by the norms. The activity of the interpreter was reduced to the retrieval of rules formally expressed in positive law or logically entailed by the system. In this respect, it is particularly interesting the case of the French School of Exegesis. Such school, whose spirit is traditionally represented by the quote attributed to Jean Joseph Bugnet “Je ne connais pas le droit civil: je n’enseigne que le Code Napoléon”, aimed at approaching the text of the Code by relying only on deductive methods and literal interpretation, quoted in Francois Gény, Méthode d’interprétation et

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determining the failure of such perspective, i.e., inexhaustive legal rules. Thanks to machines, the gaps left open by legal rules can be filled by further rules that can be formulated by making explicit that quid pluris which drives the behaviour of rule followers. Precisely by detecting, formalizing and implementing into code the rules implicitly followed by jurists, Artificial Intelligence can solve the problem of the indeterminacy of legal rules and contribute to the implementation of a complete legal system⁶³⁷.

The picture advanced by the Rule of Machines narrative seems to harken back to, and re-present in a computational fashion, some of the positions discussed with reference to the family of conceptions of the Rule of Law centred on the idea of order⁶³⁸. On one hand, common lawyers’ idea of the body of law as containing an immanent reason accessible through observation and study⁶³⁹ is reformulated by substituting the former with the data representing the body of law and the latter with machine learning techniques; accordingly, the “wisdom of the ages”⁶⁴⁰ is turned into the “wisdom of the data”⁶⁴¹. The contours of the “artificial reason of the

sources en droit privé positif, Librairie Gènèrale de Droit & de Jurisprudence, 1919, vol. I, part I, p.

30. According to the rhetoric of servility towards la lettre de la loi, each article of the Code was to be read as a theorem: the task of the jurist was to draw conclusions and ascertain the immutable and codified meaning of the law impressed by the legislator. Another exemplar case in which the ideal of the completeness of the Code proved to be unattainable is the discussion on article 4 of the Code Civil. The article provided that “Le juge qui refusera de juger, sous prétexte du silence, de l'obscurité ou de l'insuffisance de la loi, pourra être poursuivi comme coupable de déni de justice”. As Hermann Ulrich Kantorowicz, one of the fathers of the German Free Law Movement (Freirechtslehre), argued

“We can now say honestly and with some confidence that the gaps in the written law are no less important than the words!”, quoted in Paolo Grossi, A History of European Law, cit., p. 119.

In a different perspective, another experience that made evident that a complete system of law was an impracticable ideal was that of the drafting of the Allgemeines Landrecht für di Preussischen Staaten (ALR). The Emperor Frederick the Great was determined to enact a Code designed as a “body of perfect laws” in which “everything would be foreseen, everything would be combined, and nothing would be subject to absurdities”, quoted in Pierre Legrand, Strange power of words: Codification situated, cit., p. 15. The pursuit of such goal, however, turned out into an ante litteram illustration of the challenges posed by legal knowledge representation. The attempt to fit all law into written legal rules led to a contradictory result, an “elephantine mass of rules quite in contravention of the Enlightenment ideals of simplicity and clarity”, see Paolo Grossi, A History of European Law, cit., p.

87. The legal text that finally entered into force in 1794 was, more than a code, an extraordinary complex consolidation composed of more than 19.000 articles. As Alexis De Tocqueville notoriously commented, “Beneath this quite modern head there emerged […] a quite Gothic body”, see Jon Elster (ed.), Tocqueville: The Ancien Régime and the French Revolution, translated by Arthur Goldhammer, Cambridge University Press, 2011, p. 203. On the difference between codification and consolidation, see Mario E. Viora, Consolidazioni e codificazioni: Contributo alla storia della codificazione, Giappichelli, 1990.

637 One of the solutions presented, indeed is that of personalized law, micro-directives which are output by machines. On the other hand, the code-driven approach can be combined with the “rules”

derived from data-driven tools. See, Micheal A. Livermore, Rule by Rules, in Whalen Ryan (ed.), Computational Legal Studies. The Promise and Challenge of Data-Driven Research, Edward Elgar Publishing, Cheltenham, 2020, p. 238

638 Supra, §§ 1.5. ff

639 Supra, § 1.6.1.3.

640 Ivi

641 In this sense, Casey and Niblett maintain that “the judgment of one human is outweighed by the wisdom of a decision generated by predictive technology that takes into account millions of judgments

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law” blur into those of Artificial Legal Intelligence, resulting into a form of

“computational Langdellianism”⁶⁴². On the other hand, the Rule of Machines appropriates and further develop the account rule following elaborated by Hayek⁶⁴³. In this respect, Casey and Niblett discuss the inherent limitations of jurists’ capacity to identify and articulate the rules they follow providing as examples two notable arguments of American case law: on one hand, the renowned position adopted by Justice Stewart in the context of the definition of what counts as hardcore pornography under the First and Fourteenth Amendments, i.e., “I know it when I see it”⁶⁴⁴; on the other, Justice Cardozo’s statement that “[w]here the line is to be drawn between the important and the trivial cannot be settled by a formula.

'In the nature of the case precise boundaries are impossible' […]”⁶⁴⁵. According to the Authors, these cases make manifest how jurists’ implicit ability to detect distinctions is not complemented by the capacity to articulate the rules according to which such distinctions are made. On the other hand, through the processing of data, machines can detect and formalize the “laws which rule”, that is, the rules determining the behaviour of decision makers:

[m]achine-learning algorithms learn by recognizing features, concepts, principles, and ideas that humans instinctively recognize but find difficult to program or code. Rather than having to structure a program in order to code rules, the rules are crafted and understood by the artificially intelligent machine⁶⁴⁶

and decisions”, see Anthony Casey, Anthony Niblett, Self-Driving Laws, cit., p. 437; Id., The Deaths of Rules and Standards, cit., pp. 1426, 1430; Benjamin Alarie, The Path of the Law, cit., p. ___ . The Authors refer to James Surowiecki, The Wisdom of Crowds: Why the Many are Smarter than the Few and How Collective Wisdom Shapes Business, Economies, Societies and Nations, Doubleday, Anchor, 2004

642 Langdell developed an axiomatic conception of law that was inspired by the works of Wolff and Leibniz and by the model provided by natural sciences. For the American jurist, as botanists and chemists had their botanical gardens and laboratories, lawyers had libraries: in the same way scientists infer axioms from empirical observations, the legal scientist can come to know legal principles and their connections through the observation of empirical data. The data that the legal scientist has to deals with are case law. Each legal case is seen as the expression and instantiations of a set of principles that govern law at a higher level. On the basis of a careful observation and an activity of systematic ordering, the legal scientist could “compress” legal data into axioms from which the solution of legal cases could be logically deduced. See Micheal H. Hoeflich, Law and Geometry, cit., p. 120. In this sense, taking as an example the difference between independent contractors and employees, Alarie points out that, notwithstanding the number of cases dealt with,

“judges have not been able to articulate a single bright line test”. According to the Author, a precise test is available: it is implicit in the case-law and it can be detected and formalized through machines, see Benjamin Alarie, The Path of the Law: Towards Legal Singularity, cit., p. 446

643 Supra, § 2.3.1.3.

644 Jacobellis v. Ohio, 378 U.S. 184 (more)84 S. Ct. 1676; 12 L. Ed. 2d 793; 1964 U.S. LEXIS 822;

28 Ohio Op. 2d 101

645 Jacob & Youngs, Inc. v. Kent, 230 N.Y. 239 (1921) 230 N.Y. 239; 129 N.E. 889; 1921 N.Y.

LEXIS 828; 23 A.L.R. 1429

646 Anthony J. Casey, Anthony Niblett, The Death of Rules and Standards, cit., p. 1426, fn 99, emphasis added

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As it were, thanks to data-driven tools, jurists will be able to progress from “I know it when I see it” to “the machine knows it, even if I can’t see it”. But there is more:

as the Authors put it, “[d]eep-learning technology will find hidden connections in the law, elucidating principles that do – and more importantly, should – underpin the law”⁶⁴⁷.

In this way, the perspective of the Rule of Machines proves to rest on, and carries further, an all but unproblematic overlapping between legal rules and laws describing behaviour. In this respect, mutatis mutandis, one could notice a common thread linking the post-war debate on Jurimetrics⁶⁴⁸, the discussion on connectionism started from the Eighties within the AI and law community, and the themes discussed within the current debate on AI. That which unites the latter is indeed the common concerns for the relations between the empiricist perspective assumed by data-driven approaches and the normative standpoint which underpins the practice of law. Especially during the Nineties, the results of the first applications of neural networks in the legal field led the GOFAIL community to address the legitimacy of the “decisions” generated by the machine⁶⁴⁹, that is, the possibility to justify them as legal decisions in light of an explanation of the process through which they were output⁶⁵⁰. As the perspective underlying the current Rule of Machines narrative shows, not only the last two decades of research on connectionism and law have not solved the concerns with the problem of the normative significance of data-driven approaches; on the contrary, such problem has possibly been further deepened by the successes, i.e., performance, achieved by the latter especially in tasks such as binary classification⁶⁵¹. Recalling Losano, one

647 Iid, Self-Driving Laws, cit., p. 433

648 As illustrated above, already in the post-war period the research on quantitative predictions of judicial decisions encountered a set of censures pointing to the lack of relevance from a legal perspective, as exemplified by Stone’s claim that such methods would at best be capable of predicting what future decisions will, and not should, be Julius Stone, Law and the Social Science in the Second Half Centrury, cit., p. 54; see, also, Frederick Bernays Wiener, Decision Prediction by Computers:

Nonsense Cubed-and Worse, cit., p. 1023. See, supra, Chapter IV, §§ 4.3, 4.3.1.

649 David R. Warner Jr, A neural network‐based law machine: The problem of legitimacy, Information and Communications Technology Law, 1993, 2, 2, p. 135.

650 Interestingly, the account provided by Aikenhead with respect to the different methods proposed to

“get explanations and justifications from neural nets” is current debate: “(1) Extract rules from the neural net; (2) Present to the user those nodes (factors) that had a positive contributory influence along with those that had a negative contributory influence on the decision; (3) Present the training set of the neural net to the user; and (4) Create a hybrid system where the output of the neural net is explained ex post facto by other systems”. As Aikenhead put it “[…] while rules [extracted from the network] may provide an explanation of a result, it is hard to regard them as a justification”. Michael Aikenhead, The Uses and Abuses of Neural Networks in Law, in Santa Clara High Technology Law Journal 1996 12, 1, pp. 62-63; see, also, Trevor J. M. Bench-Capon, Neural Networks and Open Texture, cit., p. 292; Daniel Hunter, Out of their minds: Legal theory in neural networks, in Artificial Intelligence and Law, 1999, 7, 2-3, p. 129

651 Along the same lines, the criticism and concerns recent raised by Bench-Capon: the Author points to the limitations of data-driven approaches and emphasized the reasons why “GOFAIL still has a place in AI and Law”Trevor J. M. Bench-Capon, The Need for Good Old-Fashioned AI and Law, cit., p. 23

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might question the extent to which that which is measured and formalized on the basis of the analysis of data can be called law⁶⁵².

As I will discuss in the next section, the overlooking and blurring of the lines of different understanding of rules is one of the main reasons why not only the Rule of Machines does not solve the aporias of the Rule of Law, but actually entrenches them.