Developing conversational agents for use in criminal investigations

Hepenstal, Sam, Zhang, Leishi ORCID: https://orcid.org/0000-0002-3158-2328, Kodagoda, Neesha and Wong, B. L. William ORCID: https://orcid.org/0000-0002-3363-0741 (2021) Developing conversational agents for use in criminal investigations. ACM Transactions on Interactive Intelligent Systems, 11 (3-4) . pp. 1-35. ISSN 2160-6455 [Article] (doi:10.1145/3444369)

[img]
Preview
PDF - Final accepted version (with author's formatting)
Download (1MB) | Preview

Abstract

The adoption of artificial intelligence (AI) systems in environments that involve high risk and high consequence decision-making is severely hampered by critical design issues. These issues include system transparency and brittleness, where transparency relates to (i) the explainability of results and (ii) the ability of a user to inspect and verify system goals and constraints; and brittleness, (iii) the ability of a system to adapt to new user demands. Transparency is a particular concern for criminal intelligence analysis, where there are significant ethical and trust issues that arise when algorithmic and system processes are not adequately understood by a user. This prevents adoption of potentially useful technologies in policing environments. In this article, we present a novel approach to designing a conversational agent (CA) AI system for intelligence analysis that tackles these issues. We discuss the results and implications of three different studies; a Cognitive Task Analysis to understand analyst thinking when retrieving information in an investigation, Emergent Themes Analysis to understand the explanation needs of different system components, and an interactive experiment with a prototype conversational agent. Our prototype conversational agent, named Pan, demonstrates transparency provision and mitigates brittleness by evolving new CA intentions. We encode interactions with the CA with human factors principles for situation recognition and use interactive visual analytics to support analyst reasoning. Our approach enables complex AI systems, such as Pan, to be used in sensitive environments, and our research has broader application than the use case discussed.

Item Type: Article
Keywords (uncontrolled): Artificial Intelligence, Human-Computer Interaction
Research Areas: A. > School of Science and Technology > Computer Science
Item ID: 33823
Notes on copyright: ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of a national government. As such, the Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.
© 2021 Association for Computing Machinery.
This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in ACM Transactions on Interactive Intelligent Systems, http://dx.doi.org/10.1145/3444369
Useful Links:
Depositing User: Jisc Publications Router
Date Deposited: 13 Sep 2021 08:15
Last Modified: 04 Nov 2021 16:40
URI: https://eprints.mdx.ac.uk/id/eprint/33823

Actions (login required)

View Item View Item

Statistics

Downloads
Activity Overview
36Downloads
47Hits

Additional statistics are available via IRStats2.