Legal Conflict Detection in Interacting Legal Systems

The human reasoning process used in conducting arguments to resolve conflicts and reach a decision is an interdisciplinary study. Modelling argumentation has a great impact on the development of theories and applications in AI especially in critical domains that involve richness of reasoning such as Law. Therefore, argumentation has been recognised as a core topic in AI and Law. Developing computational argumentation systems that simulate legal reasoning requires resolving distinctive challenges that concern contextual rules, procedural issues and the interpretation of different elements. This PhD research aims to provide a supportive computational model for analysing stages in the Supreme Court starting from the oral hearings. In particular, a key aim is analysing the social values of the legal arguments from different perspectives, and finding the relation between the Court opinion and the components constructed in the Court oral hearings. This paper gives an overview of the current PhD research proposing the problem, the overall aims and the approach required to fulfil the aims. Furthermore, it provides a summary of the background in argumentation in the domain of AI and Law and presents the deliverables obtained so far. 1 Research Problem and Aims This work aims to develop a framework for deliberation through which Supreme Court oral hearings can be analysed in order to identify the components from which arguments are constructed for delivering the opinion. Following on from the analysis, a dialogue system will be defined to capture the moves made during the hearings. This dialogue system will be validated through application to selected cases in various courts. It will then be investigated how the framework can be generalised so that it can be applied to other domains in which deliberative reasoning occurs. In comparison to other contexts, dialogues in the legal domain combine arguments from different sources, i.e. argument about the case evidence and facts, argument from legal rules, argument from precedent cases, argument from hypothetical tests and others which are required to resolve the ambiguity of the conflict issues. However, the structure of exchanging arguments in legal dialogues is not clear, the argument types are interleaved and there is no particular order for the parties to pose arguments which makes the analysis of the oral hearings more complicated. Furthermore, coming to a decision in a legal case dialogues is a separate process that requires legal analysis in order to derive the case facts, apply the facts to the current law, which intended to reflect the values of society, and announce the decision that is limited to two outcomes (deciding for plaintiff and decide for defendant), in a form of Court opinion that explores the arguments supporting the decision. Modelling these aspects provide challenges in the computational development in the domain of AI and Law. Thus, in particular, this PhD research is initially attempting to fulfil the following aims: – Define a representation based on conflicts in social values that enable to show the components of the arguments for the U.S. Supreme Court oral hearing dialogues. – Provide a full analysis of the oral hearing dialogues by studying a particular legal case study using the defined representation. – Develop a dialogue system by defining speech acts and a dialogue protocol . – Analyse the subsequent Court opinion arguments by finding the relation between the argument components that emerge from the oral hearings through selecting and justifying the options. – Demonstrate the model using other Supreme court cases and other jurisdiction. – Generalise the model to handle deliberation dialogues in non-legal contexts. 2 Background Research Extensive work has enriched the domain of argumentation in AI and Law over the last 25 years [8]. The nature of legal dialogues emerge different types of arguments which result in different types of argumentation schemes such as argument from analogy, argument from expert opinions, argument from rules and others. These argumentation schemes are used extensively in modelling legal reasoning. Thus, modelling reasoning with legal cases has been a central topic of AI and Law from the beginning, and there is now a good degree of consensus, especially with regard to the main elements involved. This consensus can be expressed as a tree of inference with a legal decision as the root and with evidence as the leaves. Between the two there are a number of distinct layers. Immediately below the decision there is a level of issues [10], or values [6], which provide the reasons why the decision is made. The idea here is that laws are made (and applied) so as to promote social values: whether a value is promoted or not is an issue. Where more than one value is involved and they point to different decisions, the conflict needs to be resolved. Sometimes it is appropriate to give priority to one value over another (as in [6]), sometimes a balance needs to be struck (as in [10]). Note that the relation between issues may be seen as a matter of ordering, or requiring a balance between the values: there is as yet no consensus on this point [7]. At the next level down there are a number of factors [3]. Factors are stereotypical fact patterns which, if present in a case, favour one side or the other by promoting a value, and so are used to resolve the issues and permit comparison between the cases. Sometimes (as in [3]) it may be convenient to group several factors together under more abstract factors, so that we may have two or three layers of factors, moving from the base level factors through more abstract factors, before reaching the issues. Below the factors there are the fact patterns used to determine their presence. These facts supply reasons for and against the presence of the factor which need to be considered and weighed to make a judgement. At the lowest level there is the evidence. Facts are determined by particular items of evidence, and where evidence conflicts a judgement will need to be made: often this judgement is made by a jury of lay people rather than lawyers. In the lower courts there will be real items of evidence, but by the time a case reaches the Supreme Court, the facts are usually considered established and beyond challenge. one example of work concerned with this stage includes Gordon’s Pleadings Game [11], which identifies which facts are agreed by the parties and which will require resolution in the trial itself. Thus a complete argument for a case will comprise a view on what can be considered as evidence for relevant facts: what facts are required to establish the presence of various factors, and how they relate; how the factors can be used to determine the issues; and, where issues and values conflict, how these conflicts should be resolved. In the first stage of this research we will show how these elements relate to the individual and collective goals of the oral hearing dialogues. 3 Research Methodology Towards the main aim of establishing a deliberative reasoning framework, this research is intended to follow a qualitative method as shown in the following description. Oral Hearings Analysis and Representation The research begins by investigating the dialogue interactions in the legal domain, particularly in the U.S. Supreme Court oral hearings. After that, we analyse the oral hearing transcript using a case study from the domain of AI and Law and provide a preliminary representation of the arguments components exchanged at this stage. Based on the defined components, we also define the speech acts required to capture the moves in the oral hearings and construct the arguments representation for every party in each oral dialogue. Opinion Analysis and Representation Following the oral hearing, we define a representation for the court opinion and analyse the decision by navigating through the arguments constructed from the oral hearings. Dialogue System Toward automation for this analysis, we develop a dialogue system to support the process of constructing the argument components representation using the defined dialogue moves. Throughout this stage, we need to define the legal case ontologies for the components to provide the grammar that set out the rules for how the components can be combined and constructed. Further more, we will define the algorithm that is required to navigate through the trees to provide the analysis for the decision made and propose a protocol for the oral hearings dialogue. Evaluation In order to evaluate the system, we apply the implemented system to a number of supreme court cases and cases from other jurisdictions to identify required modifications of the ontologies and representation. Generalisation Finally, the work elements will be combined together, generalise the developed system to include deliberative dialogues in non-legal contexts, and conduct an empirical evaluation and theoretical analysis on the final version.