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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.
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While the interest in exploring the connectionist paradigm has emerged within the AI and Law community already in the late Eighties and the early Nineties575, only the more recent “virtuous circle” between the increasing availability of data, the growth of computational power and development of learning algorithms has fostered a widespread application of methods based on machine learning to the legal field576.
Through the application of such techniques, legally relevant texts are turned into data and become the direct object of quantitative statistical analysis which afford the development of a “distant reading” approach to law577. In this light, network analysis578, that is, the computational analysis of the network of links between different items of information representing, for instance, case law579 or legislation580, can be employed for purposes such as the improvement of information retrieval or the mapping of legal change581.
Xu, YunFan Shao, Ning Dai, XuanJing Huang, Pre-trained models for natural language processing:
A survey, in Science China, 2020, 63, 10, p. 1872
575 Richard K. Belew, A connectionist Approach to Conceptual Information Retrieval, in Proceedings of the First International Conference on Artificial Intelligence and Law, ACM Press, New York, 1987, p. 116; Gert-Jan van Opdorp, R.F. Walker, J.A. Schrickx, C. Groendijk, P.H. van den Berg, Network at work, a connectionist approach to non-deductive legal reasoning, in Proceedings of the Third International Conference on Artificial Intelligence and Law, ACM Press, New York, 1991, p.
278; Trevor Bench-Capon, Neural Networks and Open Texture, in Proceedings of the Fourth International Conference on Artificial Intelligence and Law, ACM Press, New York, 1993, p. 292;
John Zeleznikow, Andrew Stranieri, The Split-Up System: Integrating Neural Networks and Rule Based Reasoning in the Legal Domain, in Proceeding of the Fifth International Conference on Artificial Intelligence and Law, ACM Press, New York, 1995, p. 185
576 Harry Surden, Artificial Intelligence and Law: An Overview, 35 Georgia State University Law Review, 2019, 35, p. 1305; David Lehr, Paul Ohm, Playing with the Data: What Legal Scholars Should Learn About Machine Learning, in University of California Davis Law Review, 2017, 51, p.
653; Henry Surden, Machine Learning and Law, in Washington Law Review, 2014, 89, p. 87
577 Kevin D. Ashley, Artificial Intelligence and Legal Analytics, cit., pp. 169 ff , 234 ff; Michael A.
Livermoore, Daniel N. Rockmore (eds.), Law as Data, cit.,, p. xvii, xix; Michael A. Livermoore, Daniel N. Rockmore, Distant Reading the Law, in Id. (eds.), Law as Data, cit., p. 3; Nina Varsava, Computational legal studies, digital humanities, and textual analysis, in Ryan Whalen (ed.), Computational legal studies, cit., p. 29; Franco Moretti, Distant Reading, Verso, London, 2013; See, Artificial Intelligence and Law, Special Issue: Legal Text Analytics, 2018, 26, 2
578 Radboud Winkels, Nicola Lettieri, Sebastiano Faro (eds.), Network Analysis in Law, Edizioni Scientifiche Italiane, Napoli, 2014
579 Dafne van Kuppevelt, Gijs van Dijck and Marcel Schaper, Purposes and challenges of legal network analysis on case law, in Ryan Whalen (ed.), Computational legal studies, cit., p. 265; Henrik Palmer Olsen and Magnus Esmark, Needles in a haystack: using network analysis to identify cases that are cited for general principles of law by the European Court of Human Rights, in Ryan Whalen (ed.), Computational legal studies, cit., p. 293
580 Kevin D. Ashley, Artificial Intelligence and Legal Analytics, cit., p. 70; Adam Badawi, Giuseppe Dari-Mattiacci, Reference Networks and Civil Codes, in Michael A. Livermoore, Daniel N. Rockmore (eds.), Law as Data, cit., p. 335; p. 143; Romain Boulet, Pierre Mazzega, Danièle Bourcier, Network approach to the French system of legal codes part II: the role of the weights in a network, in Artificial Intelligence and Law, 2018, 26, 1, p. 23; Id., (2011) A network approach to the French system of legal codes—part I: analysis of a dense network, in Artificial Intelligence and Law, 2011, 19, 4, p.
333; Marios Koniaris, Ioannis Anagnostopoulos, Yannis Vassiliou, Legislation as a complex network:
Modelling and analysis of European Union legal sources, in Rinke Hoekstra (ed.), Legal Knowledge and Information Systems, IOS Press, Amsterdam, 2014
581 Kevin D. Ashley, Artificial Intelligence and Legal Analytics, cit., p. 354
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Particular attention has been paid to the data-driven techniques developed for the prediction of legal outcomes. “Quantitative Legal Predictions”582 have been identified as the instrument for answering a multifaceted array of questions, such as
Do I have a case? What is our likely exposure? How much is this going to cost? Are these documents relevant? What will happen if we leave this particular provision out of this contract? How can we best staff this particular legal matter?583
The adoption of Machine Learning and Deep Learning techniques in the field of computational legal predictions has marked a change of direction which, on one hand, deviates from the approaches developed by the AI and Law community584 and, on the other, gets closer to the perspective of Jurimetrics. In this respect, the focus of data-driven predictions is strongly centred on predictive performance and, in some cases, the latter supersedes the prima facie relevance of the data used to train the predictive system. Whether, on one hand, some approaches are based on the annotation of the text of legal decisions or also of the arguments thereby contained585, on the other hand, accurate predictive models are built on the basis of metadata, i.e., data concerning judges, time of decision, etc.586, or behavioural data such as attorney attorneys’ vocal features “in the first three seconds of speech”587.
582 Daniel. M. Katz, Quantitative Legal Prediction—or—How I Learned to Stop Worrying and Start Preparing for the Data-Driven Future of the Legal Services Industry, in Emory Law Journal, 2013, 62, p. 909
583 Ivi, pp. 912; 928. For an overview of the debate concerning the use of such tools by legal practitioners, see: Richard Susskind, Tomorrow’s Lawyer. An Introduction to Your Future, Oxford University Press, 2017; Dana A. Remus, Frank. Levy, Can Robots be Lawyers? Computers, Lawyers and the Practice of Law, in Georgetown Journal of Legal Ethics, 2017, 30, 3, p. 501; Drury D.
Stevenson, Nicholas J. Wagoner, Bargaining in the Shadow of Big Data, in Florida Law Review, 2016, 67, 4, p. 1337; Dana. A. Remus, The Uncertain Promise of Predictive Coding, in Iowa Law Review, 2014, 99, p. 1691
584 As the prediction systems based on case-based reasoning models, for an overview, see Kevin D.
Ashley, Artificial Intelligence and Legal Analytics, cit., pp. 107 ff
585 Kevin D. Ashley, Artificial Intelligence and Legal Analytics, cit., pp. 285 ff; Masha Medvedeva, Michel Vols, Martijn Wieling, Using machine learning to predict decisions of the European Court of Human Rights, in Artificial Intelligence and Law, 2020, 28, p. 237; Haoxi Zhong, Zhipeng Guo, Cunchao Tu, Chaojun Xiao, Zhiyuan Liu, Maosong Sun, Legal Judgment Prediction via Topological Learning, in Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, Association for Computational Linguistics, Brussels, 2018, p. 3540; Aletras, Nikolaos, Dimitrios Tsarapatsanis, Daniel Preoţiuc-Pietro, Vasileios Lampos, Predicting judicial decisions of the European Court of Human Rights: a Natural Language Processing perspective, in PeerJ Computer Science, 2016, 2, 93; for a discussion of the latter article, see Mireille Hildebrandt, Algorithmic Regulation and the rule of law, in Philosophical Transactions of the Royal Society A, 2018, 378, pp. 6-7; Frank Pasquale, Glyn Cashwell, Prediction, Persuasion, and the Jurisprudence of Behaviourism, in University of Toronto Law Journal, 2018, 68, 1, p. 63; Trevor J. M. Bench-Capon, The Need for Good Old-Fashioned AI and Law, in Walter Hötzendorfer, Christof Tschohl, Franz Kummer (eds), International Trends in Legal Informatics: A Festschrift for Erich Schweighofer, NOVA MD, Vachendorf, 2020, p. 23
586 Daniel Martin Katz, Michael J. Bommarito II, Josh Blackman, A general approach for predicting the behavior of the Supreme Court of the United States, in PLoS ONE, 2017, 12, 4; Kevin D. Ashley, Artificial Intelligence and Legal Analytics, cit., pp. 111 ff
587 Daniel L. Chen, Yosh Halberstam, Manoj Kumar, Alan C. L. Yu, Attorney Voice and the Supreme Court, in Michael A. Livermoore, Daniel N. Rockmore (eds.), Law as Data, cit., p. 367
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Beside the prediction of judicial outcomes, classification tools have been deployed even for the automatic identification and judges’ modes of moral reasoning588. Alongside practice-oriented applications, the advances in computational techniques and the growing availability of data have driven the development of tools and conceptual approaches directed at framing and analysing the legal phenomenon through a scientific, i.e., empirical, enabling to explain and understand “law as a fact” 589 and investigate “the intricate networks of cognitive and social mechanisms through which law emerges, is applied, and exerts its effects590. In this sense, the computational turn in legal scholarship591 extends to law of the interdisciplinary approach which distinguishes the Computational Social Sciences (CSS)592 and the latter’s assumption that “[i]nformation and communication technologies can greatly enhance the possibility to uncover the laws of the society”593.
588 Keith Carlson, Daniel N. Rockmore, Allen Riddell, John Ashley, Micheal A. Livermoore, Style and Substance on the US Supreme Court, in Michael A. Livermoore, Daniel N. Rockmore (eds.), Law as Data, cit., p. 83; Jens Frankenreiter, Writing Style and Legal Tradition, in Michael A. Livermoore, Daniel N. Rockmore (eds.), Law as Data, cit., p. 151; Nischal Mainali, Liam Meier, Elliott Ash, Daniel L. Chen, Automated classification of modes of moral reasoning in judicial decisions, in Ryan Whalen (ed.), Computational legal studies, cit., p. 77
589 Nicola Lettieri, Law in Turing’s Cathedral: Notes on the Algorithmic Turn of the Legal Universe, in Woodrow Barfield (ed.), The Cambridge Handbook of the Law of Algorithms, Cambridge University Press, 2020, pp. 695, 719; Sebastiano Faro, Nicola Lettieri, Walking Finelines between Law and Computational Social Science, in Informatica e diritto, 2013, 22, 1, p. 16
590 Nicola Lettieri, Law in Turing’s Cathedral, cit., p. 719 .As Lettieri highlights: “Based on the identification of the scientific explanation with the reproduction “in silico” (that is, in a computer simulation) of the social processes being investigated, ABM underlies a generative approach to research in which social macrodynamics and structures are interpreted, described, reproduced, and then explained as the result of micro-interactions between computational entities (agents) simulating the behavior of real individuals”, Nicola Lettieri, Law in Turing’s Cathedral, cit., p. 711-713; Bruce Edmonds, What Social Simulation Might Tell Us about How Law Works, in Informatica e diritto, 2013, 12, 1, pp. 47; Martin Neumann, The cognitive legacy of norm simulation, in Artificial Intelligence and Law, 2012, 20, 4, p. 339. In general, see the special Issues of Artificial Intelligence and Law: Simulations, norms and laws, 2012, 20, 4 and 2013, 21, 1
591 Ryan Whalen, The emergence of computational legal studies: an introduction, in Id. (ed.), Computational Legal Studies. The Promise and Challenge of Data-Driven Research, Edward Elgar Publishing, Cheltenham, 2020, p. 1; Ginevra Peruginelli, Sebastiano Faro (eds.), Knowledge of the Law in the Big Data Age, IOS Press, 2019; Micheal A. Livermoore, Daniel N. Rockmore (eds.), Law as Data. Computation, Text, and the Future of Legal Analysis, Santa Fe Institute Press, Santa Fe, 2019; Nicola Lettieri, Law in Turing’s Cathedral: Notes on the Algorithmic Turn of the Legal Universe, in Woodrow Barfield (ed.), The Cambridge Handbook of the Law of Algorithms, Cambridge University Press, 2020, p. 691
592 Claudio Cioffi-Revilla, Computational Social Science, in WIREs Computational Statistics, 2010, 2, 3, pp. 259-271; David Lazer, Alex (Sandy) Pentland, Lada Adamic, Sinan Aral, Albert Laszlo Barabasi, Devon Brewer, Nicholas Christakis, Noshir Contractor, James Fowler, Myron Gutmann, Tony Jebara, Gary King, Michael Macy, Deb Roy, Marshall Van Alstyne, Computational Social Science, in Science, 2009, 323, 5915, pp. 721-723; David M. J. Lazer, Alex Pentland, Duncan J.
Watts, Sinan Aral, Susan Athey, Noshir Contractor, Deen Freelon, Sandra Gonzalez-Bailon, Gary King, Helen Margetts, Alondra Nelson, Matthew J. Salganik, Markus Strohmaier, Alessandro Vespignani, Claudia Wagner, Computational social science: Obstacles and opportunities, in Science, 2020, 369, 6507, p. 1060-1062
593 Rosaria Conte, Nigel Gilbert, Giulia Bonelli, Claudio Cioffi-Revilla, Guillaume Deffuant, Janos Kertesz, Vittorio Loreto, Suzy Moat, Jean-Pierre Nadal, Angel Sanchez, Martin A. Nowak, Andreas Flache, Maxi San Miguel, Dirk Helbing, Manifesto of Computational Social Science, in The European Physical Journal Special Topics, 2012, 214, pp. 327
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