Korea Criminal Intelligence Review

2020 ◽  
Keyword(s):  
Criminal Intelligence

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

2020 ◽  
Vol 64 (1) ◽  
pp. 239-243
Author(s):  
Sam Hepenstal ◽  
Leishi Zhang ◽  
Neesha Kodogoda ◽  
B.L. William Wong
Keyword(s):  
Artificial Intelligence ◽  
Task Analysis ◽  
Autonomous Agents ◽  
Cognitive Task ◽  
Cognitive Task Analysis ◽  
Criminal Investigation ◽  
Event Tree ◽  
Analysis Method ◽  
Ethical Concerns ◽  
Criminal Intelligence

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.

scholarly journals Enhancement for graph operations in relational database for criminal intelligence domain

2021 ◽  
Vol 14 (1) ◽  
pp. 49
Author(s):  
Marcin Łoś ◽  
Robert Marcjan ◽  
Mateusz Piech
Keyword(s):  
Relational Database ◽  
Graph Operations ◽  
Criminal Intelligence

scholarly journals Crime mapping features

2020 ◽  
Vol 79 (4) ◽  
pp. 66-72
Author(s):  
О. В. Манжай ◽  
А. О. Потильчак
Keyword(s):  
Theoretical Basis ◽  
Hot Spot ◽  
Kernel Density ◽  
Crime Mapping ◽  
Density Mapping ◽  
Criminal Intelligence ◽  
Symbol Mapping ◽  
Near Repeat ◽  
Artificial Intelligence Systems ◽  
Clinical Strategy

In this paper tools, organization and tactics of crime mapping are analyzed. The directions of application of mapping for maintenance of public safety and order, in criminal intelligence process, etc. are outlined. The domestic experience of mapping is briefly analyzed. The main goals that are achieved with the use of mapping are defined. Features of visualization of criminogenic cells are revealed. Pin mapping features (when points which symbolize a certain event are placed on the map on the corresponding coordinates) are outlined. Kernel density mapping is described, which makes it much easier to detect criminogenic foci, as hot-spot maps clearly reflect the concentration of certain events in the region. A method of mapping using proportional symbol mapping is disclosed when the increase in the size of the symbol denoting a point on the map is proportional to the increase in the number of events or other parameters at these coordinates. The building of geographical profiles of criminals is briefly described. The theoretical basis of mapping for the prediction of crimes is outlined. Prediction strategies based on equations and machine calculations and actuarial strategies based on expertise and clinical strategy are analyzed. Considerations are given to the appropriateness of applying appropriate strategies in different countries. The phenomenon of near repeat patterns is studied. Some software solutions for the implementation of the tasks of mapping criminal manifestations and the use of artificial intelligence systems for this purpose are described. Examples are given. It is noted that the use of cartography to prevent and predict crimes in Ukraine is in its infancy. Some solutions are proposed that could improve the situation in the field of crime mapping in Ukraine.

Protecting procedural fairness and criminal intelligence

2015 ◽  
pp. 75-98
Author(s):  
Gabrielle Appleby
Keyword(s):  
Procedural Fairness ◽  
Criminal Intelligence
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