Developing conversational agents for use in criminal investigations

Journal article


Hepenstal, S., Zhang, L., Kodagoda N. and Wong B.L.W 2021. Developing conversational agents for use in criminal investigations. ACM Transactions on Interactive Intelligent Systems. 11 (3-4), pp. 1-35. https://doi.org/10.1145/3444369
AuthorsHepenstal, S., Zhang, L., Kodagoda N. and Wong B.L.W
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 paper, 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.

KeywordsExplainability; Criminal intelligence analysis; Conversational agents; Transparency
Year2021
JournalACM Transactions on Interactive Intelligent Systems
Journal citation11 (3-4), pp. 1-35
PublisherACM
ISSN2160-6455
Digital Object Identifier (DOI)https://doi.org/10.1145/3444369
Official URLhttps://dl.acm.org/doi/abs/10.1145/3444369
Publication dates
Online31 Aug 2021
Publication process dates
Accepted01 Dec 2020
Deposited03 Nov 2021
Accepted author manuscript
Output statusPublished
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A granular computing approach to provide transparency of intelligent systems for criminal investigations
Zhang, L. 2021. A granular computing approach to provide transparency of intelligent systems for criminal investigations. in: Pedrycz, W. and Chen, S.-M. (ed.) Interpretable Artificial Intelligence: A Perspective of Granular Computing Cham Springer.