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A methodology for designing systems to reason with legal cases using Abstract Dialectical Frameworks

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Abstract

This paper presents a methodology to design and implement programs intended to decide cases, described as sets of factors, according to a theory of a particular domain based on a set of precedent cases relating to that domain. We use Abstract Dialectical Frameworks (ADFs), a recent development in AI knowledge representation, as the central feature of our design method. ADFs will play a role akin to that played by Entity–Relationship models in the design of database systems. First, we explain how the factor hierarchy of the well-known legal reasoning system CATO can be used to instantiate an ADF for the domain of US Trade Secrets. This is intended to demonstrate the suitability of ADFs for expressing the design of legal cased based systems. The method is then applied to two other legal domains often used in the literature of AI and Law. In each domain, the design is provided by the domain analyst expressing the cases in terms of factors organised into an ADF from which an executable program can be implemented in a straightforward way by taking advantage of the closeness of the acceptance conditions of the ADF to components of an executable program. We evaluate the ease of implementation, the performance and efficacy of the resulting program, ease of refinement of the program and the transparency of the reasoning. This evaluation suggests ways in which factor based systems, which are limited by taking as their starting point the representation of cases as sets of factors and so abstracting away the particular facts, can be extended to address open issues in AI and Law by incorporating the case facts to improve the decision, and by considering justification and reasoning using portion of precedents.

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Notes

  1. ADFs are formally defined in Definition 1, Sect. 2.3.

  2. No explanation for using a different number of cases for CATO and IBP-model is given in Brüninghaus and Ashley (2003).

  3. Whereas PADFs were designed specifically to reflect Preference-Based Frameworks (Amgoud and Cayrol 1998), the approach taken here reflects Value-Based Frameworks (Bench-Capon 2003), which are more commonly used in AI and Law. The relationship between Preference- and Value-Based Frameworks is formally characterised in Modgil et al. (2011).

  4. The Boeing Company v. Sierracin Corporation, 108 Wash.2d 38, 738 P.2d 665 (1987).

  5. Goldberg v. Medtronic, 686 F.2d 1219 (7th Cir. 1982).

  6. Running a version of Goldberg without F27 finds for the plaintiff.

  7. Carroll v United States, 267 U.S. 132 (1925).

  8. California v. Carney, 471 US 386 (1985).

  9. Coolidge v. New Hampshire, 403 U.S. 443 (1971).

  10. United States v Chadwick, 433 U. S. 1 (1977).

  11. California v. Acevedo, 500 U.S. 565 (1991).

  12. In the sense of Bench-Capon (2003).

  13. As evidenced by a spate of articles such as the Role of AI in Law, published in The Times newspaper and available at http://raconteur.net/business/time-for-technology-to-take-over, and the many discussion threads on LinkedIn group for the International Association for AI and Law.

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Acknowledgments

Would like to thank the anonymous reviewers of the first version of this paper, whose comments have helped us to clarify our ideas and significantly improve the paper.

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Authors and Affiliations

  1. Department of Computer Science, University of Liverpool, Ashton Street, Liverpool, L69 3BX, UK

    Latifa Al-Abdulkarim, Katie Atkinson & Trevor Bench-Capon

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  1. Latifa Al-Abdulkarim
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  2. Katie Atkinson
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  3. Trevor Bench-Capon
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Correspondence to Latifa Al-Abdulkarim.

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Al-Abdulkarim, L., Atkinson, K. & Bench-Capon, T. A methodology for designing systems to reason with legal cases using Abstract Dialectical Frameworks. Artif Intell Law 24, 1–49 (2016). https://doi.org/10.1007/s10506-016-9178-1

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