An approach to human-machine teaming in legal investigations using anchored narrative visualisation and machine learning

During legal investigations, analysts typically create external representations of an investigated domain as resource for cognitive offloading, reflection and collaboration. For investigations involving very large numbers of documents as evidence, creating such representations can be slow and costly, but essential. We believe that software tools, including interactive visualisation and machine learning, can be transformative in this arena, but that design must be predicated on an understanding of how such tools might support and enhance investigator cognition and team-based collaboration. In this paper, we propose an approach to this problem by: (a) allowing users to visually externalise their evolving mental models of an investigation domain in the form of thematically organized Anchored Narratives; and (b) using such narratives as a (more or less) tacit interface to cooperative, mixed initiative machine learning. We elaborate our approach through a discussion of representational forms significant to legal investigations and discuss the idea of linking such representations to machine learning

Assessing Evidence Relevance by Disallowing Assessment

Guidelines for assessing whether potential evidence is relevant to some argument tend to rely on criteria that are subject to well-known biasing effects. We describe a framework for argumentation that does not allow participants to directly decide whether evidence is potentially relevant to an argument---instead, evidence must prove its relevance through demonstration. This framework, called WG-A, is designed to translate into a dialogical game playable by minimally trained participants

Explaining classifiers’ outputs with causal models and argumentation

We introduce a conceptualisation for generating argumentation frameworks (AFs) from causal models for the purpose of forging explanations for mod-els’ outputs. The conceptualisation is based on reinterpreting properties of semantics of AFs as explanation moulds, which are means for characterising argumentative relations. We demonstrate our methodology by reinterpreting the property of bi-variate reinforcement in bipolar AFs, showing how the ex-tracted bipolar AFs may be used as relation-based explanations for the outputs of causal models. We then evaluate our method empirically when the causal models represent (Bayesian and neural network) machine learning models for classification. The results show advantages over a popular approach from the literature, both in highlighting specific relationships between feature and classification variables and in generating counterfactual explanations with respect to a commonly used metric

Constructing Bayesian Network Graphs from Labeled Arguments

Bayesian networks (BNs) are powerful tools that are well-suited for reasoning about the uncertain consequences that can be inferred from evidence. Domain experts, however, typically do not have the expertise to construct BNs and instead resort to using other tools such as argument diagrams and mind maps. Recently, a structured approach was proposed to construct a BN graph from arguments annotated with causality information. As argumentative inferences may not be causal, we generalize this approach to include other types of inferences in this paper. Moreover, we prove a number of formal properties of the generalized approach and identify assumptions under which the construction of an initial BN graph can be fully automated

Narrative and Lyrical Elements in International Investment Agreements: Towards an Imagination-Inspired Understanding of International Legal Obligations

This article applies literary analysis to the unconventional subject of International Investment Agreements (IIAs), treating these sources of international law as if they were works of fiction with a view to uncovering insights into how they might be received by their readers. It proposes that IIAs may be imaginatively appreciated both for their narrative features (their capacity to tell stories in the tradition of novels or plays) as well as their lyrical ones (their poetic or figurative elements). Rather than leading to any concrete conclusions concerning how IIAs may have been misunderstood because of readers’ neglect of these instruments’ literariness or how they should thereby be construed going forward, the article calls upon readers of IIAs to be more aware of the feelings which these instruments might inspire, much as we would expect from novels or poems. This could in turn enhance our understanding of these treaties are be viewed by the legal practitioners who draft and interpret them as well as the people whose rights they affect

Research in progress: report on the ICAIL 2017 doctoral consortium

This paper arose out of the 2017 international conference on AI and law doctoral consortium. There were five students who presented their Ph.D. work, and each of them has contributed a section to this paper. The paper offers a view of what topics are currently engaging students, and shows the diversity of their interests and influences

Computational Argumentation-based Chatbots: a Survey

The article archived on this institutional repository is a preprint. It has not been certified by peer review.Chatbots are conversational software applications designed to interact dialectically with users for a plethora of different purposes. Surprisingly, these colloquial agents have only recently been coupled with computational models of arguments (i.e. computational argumentation), whose aim is to formalise, in a machine-readable format, the ordinary exchange of information that characterises human communications. Chatbots may employ argumentation with different degrees and in a variety of manners. The present survey sifts through the literature to review papers concerning this kind of argumentation-based bot, drawing conclusions about the benefits and drawbacks that this approach entails in comparison with standard chatbots, while also envisaging possible future development and integration with the Transformer-based architecture and state-of-the-art Large Language models