An Approach to Human Machine Teaming in Legal Investigations Using Anchored Narrative Visualisation and Machine Learning.

TLDR: This paper proposes an approach to this problem by allowing users to visually externalise their evolving mental models of an investigation domain in the form of thematically organized Anchored Narratives and using such narratives as a (more or less) tacit interface to cooperative, mixed initiative machine learning.

Abstract: 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.