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Thirty years of Artificial Intelligence and Law: the second decade

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A Correction to this article was published on 05 November 2022

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Abstract

The first issue of Artificial Intelligence and Law journal was published in 1992. This paper provides commentaries on nine significant papers drawn from the Journal’s second decade. Four of the papers relate to reasoning with legal cases, introducing contextual considerations, predicting outcomes on the basis of natural language descriptions of the cases, comparing different ways of representing cases, and formalising precedential reasoning. One introduces a method of analysing arguments that was to become very widely used in AI and Law, namely argumentation schemes. Two relate to ontologies for the representation of legal concepts and two take advantage of the increasing availability of legal corpora in this decade, to automate document summarisation and for the mining of arguments.

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Notes

  1. See Sect. 6 of Villata et al. (2022), elsewhere in this issue.

  2. See Sects. 6 and 7 of Governatori et al. (2022), elsewhere in this issue.

  3. See Sect. 4 of Araszkiewicz et al. (2022), elsewhere in this issue, for a discussion of Walton’s papers in AI and Law journal and Atkinson et al. (2020a) for his overall influence on the field.

  4. See Sect. 9 of Governatori et al. (2022).

  5. See Sects. 6 and 7 of Governatori et al. (2022), elsewhere in this issue.

  6. The other article on argumentation schemes that appeared in the same issue as Verheij (2003c) is Bex et al. (2003), which is discussed elsewhere in this issue (Villata et al. (2022), Sect. 3).

  7. Several of these papers are discussed in Governatori et al. (2022), elsewhere in this issue.

  8. Although many authors, including Walton, have claimed something like this (cf. e.g. Bex et al. (2003)), Walton himself never considered argumentation schemes as purely (domain-specific) rules, but rather as dialogical or dialectical devices, where the critical questions are a key component of the scheme. Cf. Atkinson and colleagues’ recent paper on the influence of Walton on AI and Law (Atkinson et al. 2020a). See also the discussion of Walton’s papers in Sect. 4 of Araszkiewicz et al. (2022), elsewhere in this issue.

  9. This figure and the others in this section were made using Verheij’s argumentation software ArguMed based on DEFLOG, which is still available from his website https://www.ai.rug.nl/~verheij/aaa/argumed3.htm (last accessed 12-2-2022). See also (Verheij 2003a).

  10. In formal argumentation, this notion of undercutting is now fairly standard, cf. (Prakken 2010).

  11. In formal argumentation, such an attack on a premise is sometimes called undermining, and an argument that attacks the conclusion is called a rebutter (Prakken 2010).

  12. For a discussion, see Sect. 3 of Araszkiewicz et al. (2022), elsewhere in this volume.

  13. The paper appeared as part of a special issue on Ontologies for Law (Breuker et al. 2004), which indicates the great interest in the topic at the time. Elsewhere in this issue, Sect. 2 of Araszkiewicz et al. (2022) discusses the development of ontolgies in AI and Law over the decades.

  14. This department, part of the Law faculty, has hosted for two decades a handful of researchers coming from Psychology, Legal studies, Artificial Intelligence and Computer Science. In 2017 its flag passed to the Leibniz Institute, spanning over the faculties of Law and Science of UvA, and TNO, the Dutch organization for applied research.

  15. In terms of interest, today we live a similar heyday (Francesconi 2022), albeit very different approaches are being used: what is understood today by a general audience by the term Artificial Intelligence is most probably some machine-learning-based, data-driven approaches, whereas RegTech and similar technologies are much more related to distributed systems than normative systems research.

  16. For instance, to accept that humans do not typically reflect on their conduct before taking decisions (e.g. the neurological evidence in Daniel (2002)) would map our view of the world to some form of emotional determinism, which would undermine many of the (fictional, possibly illusory) constructs that allow our societies to be maintained.

  17. A more recent proposal in this direction is UFO-L (Griffo et al. 2016).

  18. Furthermore, a general disillusionment emerged, even more in practical settings, towards semantic web technologies, for their inability to handle (normative) reasoning in a scalable way.

  19. A number of criticisms have been put forward: e.g. Bench-Capon (2020) cites, as well as lack of explanations, the bias and mistakes present in past cases, the fact that the law may have evolved so that past decisions may have been made with different understandings of the law at different times, and the fact that the law is subject to change in the future. Medvedeva et al. (2020) shows that performance degrades as the dataset ages. Bex and Prakken (2021) demonstrate that it is not rational to follow predictions blindly, even given a high level of accuracy, and Steging et al. (2021) show that high accuracy can be achieved even when the underlying rationale is flawed. Many of these problems can be mitigated if the predictions are explainable, by giving a justification in legal terms, but this requires in principle some form of knowledge model.

  20. e.g. in Europe, the Artificial Intelligence Act, https://oeil.secure.europarl.europa.eu/oeil/popups/ficheprocedure.do?reference=2021/0106(COD) &l=en.

  21. The HOLJ corpus comprises 188 judgments from the years 2001–2003 from the House of Lords website. The authors extracted the judgements, removed the HTML tags, and assigned two types of label to each sentence: the rhetorical role and a relevance metric.

  22. Data is not publicly available.

  23. https://www.theguardian.com/technology/2016/oct/24/artificial-intelligence-judge-university-college-london-computer-scientists about Aletras et al. (2016), https://www.nrc.nl/nieuws/2020/12/30/robot-weet-welke-uitspraak-het-hof-zal-doen-a4025683 about Medvedeva et al. (2020) (see Sect. 6 of Villata et al. (2022), elsewhere in this issue) and quite recently https://www.dailymail.co.uk/news/article-10346933/China-develops-AI-prosecutor-press-charges-97-accuracy.html.

  24. https://www.cnbc.com/2017/02/17/lawyers-could-be-replaced-by-artificial-intelligence.html.

  25. https://www.bbc.com/news/technology-41829534.

  26. See Sect. 7 of Villata et al. (2022), elsewhere in this issue.

  27. Google Scholar gives 419 citations to the paper. Date of access: 16 May 2022.

  28. https://2021.argmining.org/index.html#previous_workshops.

  29. https://research.ibm.com/interactive/project-debater/.

  30. https://www.dagstuhl.de/en/program/calendar/semhp/?semnr=16161.

  31. See particularly the IBM Debater Datasets: https://research.ibm.com/haifa/dept/vst/debating_data.shtml.

  32. The case was the subject of a 2004 comic documentary film, Up For Grabs, https://www.imdb.com/title/tt0420356/.

  33. See Sect. 6 of Villata et al. (2022), elsewhere in this issue.

  34. See Governatori et al. (2022), Sect. 9, elsewhere in this issue.

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Authors and Affiliations

  1. University of Bologna, Bologna, Italy

    Giovanni Sartor

  2. European University Institute, Fiesole, Italy

    Giovanni Sartor

  3. Jagiellonian University, Krakow, Poland

    Michał Araszkiewicz

  4. University of Liverpool, Liverpool, UK

    Katie Atkinson & Trevor Bench-Capon

  5. Utrecht University, Utrecht, The Netherlands

    Floris Bex & Henry Prakken

  6. Tilburg University, Tilburg, The Netherlands

    Floris Bex

  7. University of Amsterdam, Amsterdam, The Netherlands

    Tom van Engers & Giovanni Sileno

  8. National Research Council of Italy (IGSG-CNR), Florence, Italy

    Enrico Francesconi

  9. European Parliament, Luxembourg, Luxembourg

    Enrico Francesconi

  10. University of Groningen, Groningen, The Netherlands

    Henry Prakken

  11. Thomson Reuters, Saint Paul, USA

    Frank Schilder

  12. University of Swansea, Swansea, UK

    Adam Wyner

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Sartor, G., Araszkiewicz, M., Atkinson, K. et al. Thirty years of Artificial Intelligence and Law: the second decade. Artif Intell Law 30, 521–557 (2022). https://doi.org/10.1007/s10506-022-09326-7

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