Providing a foundation for interpretable autonomous agents through elicitation and modeling of criminal investigation pathways

Criminal investigations are guided by repetitive and time-consuming information retrieval tasks, often with high risk and high consequence. If Artificial intelligence (AI) systems can automate lines of inquiry, it could reduce the burden on analysts and allow them to focus their efforts on analysis. However, there is a critical need for algorithmic transparency to address ethical concerns. In this paper, we use data gathered from Cognitive Task Analysis (CTA) interviews of criminal intelligence analysts and perform a novel analysis method to elicit question networks. We show how these networks form an event tree, where events are consolidated by capturing analyst intentions. The event tree is simplified with a Dynamic Chain Event Graph (DCEG) that provides a foundation for transparent autonomous investigations.

Hepenstal S, Zhang L, Kodogoda N, William Wong BL. Providing a foundation for interpretable autonomous agents through elicitation and modeling of criminal investigation pathways. Proceedings of the Human Factors and Ergonomics Society Annual Meeting. 2020;64(1):239-243. Copyright © 2020 by Human Factors and Ergonomics Society. DOI: 10.1177/1071181320641057