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

Attfield, Simon ORCID: https://orcid.org/0000-0001-9374-2481, Fields, Bob ORCID: https://orcid.org/0000-0003-1117-1844, Windridge, David ORCID: https://orcid.org/0000-0001-5507-8516 and Xu, Kai ORCID: https://orcid.org/0000-0003-2242-5440 (2019) An approach to human-machine teaming in legal investigations using anchored narrative visualisation and machine learning. Conrad, Jack G., Pickens, Jeremy, Jones, Amanda, Baron, Jason R. and Henseler, Hans, eds. Proceedings of the First International Workshop on AI and Intelligent Assistance for Legal Professionals in the Digital Workplace (LegalAIIA 2019). June 17, 2019. Montreal, QC, Canada. In: First International Workshop on AI and Intelligent Assistance for Legal Professionals in the Digital Workplace (LegalAIIA 2019)., 17 Jun 2019, Montreal, Canada. . ISSN 1613-0073

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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.

Item Type: Conference or Workshop Item (Paper)
Additional Information: Volume: 2484
Research Areas: A. > School of Science and Technology > Computer Science
Item ID: 28664
Notes on copyright: Copyright © 2019 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0). Published at http://ceur-ws.org.
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Depositing User: Simon Attfield
Date Deposited: 07 Jan 2020 09:52
Last Modified: 12 Jan 2020 17:50
URI: https://eprints.mdx.ac.uk/id/eprint/28664

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