Argumentation in Legal Reasoning

ARGUMENTATION IN ARTIFICIAL INTELLIGENCE, pages 363-82, I. Rahwan and G. Simari, eds., Springer, July 2009

Posted: 13 Nov 2010

See all articles by Trevor Bench-Capon

Trevor Bench-Capon

affiliation not provided to SSRN

Henry Prakken

University of Utrecht

Giovanni Sartor

European University Institute Law Department

Date Written: July 1, 2009


A popular view of what Artificial Intelligence can do for lawyers is that it can do no more than deduce the consequences from a precisely stated set of facts and legal rules. This immediately makes many lawyers sceptical about the usefulness of such systems: this mechanical approach seems to leave out most of what is important in legal reasoning. A case does not appear as a set of facts, but rather as a story told by a client. For example, a man may come to his lawyer saying that he had developed an innovative product while working for Company A. Now Company B has made him an offer of a job, to develop a similar product for them. Can he do this? The lawyer firstly must interpret this story, in the context, so that it can be made to fit the framework of applicable law. Several interpretations may be possible. In our example it could be seen as being governed by his contract of employment, or as an issue in Trade Secrets law. Next the legal issues must be identified and the pros and cons of the various interpretations considered with respect to them. Does his contract include a non-disclosure agreement? If so, what are its terms? Was he the sole developer of the product? Did Company A support its development? Does the product use commonly known techniques? Did Company A take measures to protect the secret? Some of these will favour the client, some the Company. Each interpretation will require further facts to be obtained. For example, do the facts support a claim that the employee was the sole developer of the product? Was development work carried out in his spare time? What is the precise nature of the agreements entered into? Once an interpretation has been selected, the argument must be organised into the form considered most likely to persuade, both to advocate the client’s position and to rebut anticipated objections. Some precedents may point to one result and others to another. In that case, further arguments may be produced to suggest following the favourable precedent and ignoring the unfavourable one. Or the rhetorical presentation of the facts may prompt one interpretation rather than the other. Surely all this requires the skill, experience and judgement of a human being? Granted that this is true, much effort has been made to design computer programs that will help people in these tasks, and it is the purpose of this chapter to describe the progress that has been made in modelling and supporting this kind of sophisticated legal reasoning.

We will review systems that can store conflicting interpretations and that can propose alternative solutions to a case based on these interpretations. We will also describe systems that can use legal precedents to generate arguments by drawing analogies to or distinguishing precedents. We will discuss systems that can argue why a rule should not be applied to a case even though all its conditions are met. Then there are systems that can act as a mediator between disputing parties by structuring and recording their arguments and responses. Finally we look at systems that suggest mechanisms and tactics for forming arguments.

Suggested Citation

Bench-Capon, Trevor and Prakken, Henry and Sartor, Giovanni, Argumentation in Legal Reasoning (July 1, 2009). ARGUMENTATION IN ARTIFICIAL INTELLIGENCE, pages 363-82, I. Rahwan and G. Simari, eds., Springer, July 2009. Available at SSRN:

Trevor Bench-Capon

affiliation not provided to SSRN ( email )

Henry Prakken

University of Utrecht ( email )

Vredenburg 138
NL-3508 TC Utrecht, 3511 BG

Giovanni Sartor (Contact Author)

European University Institute Law Department ( email )

Via Bolognese 156 (Villa Salviati)
50-139 Firenze

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