Abstract
Intelligence analysts frequently find themselves in situations of high uncertainty and ambiguity. The characteristics of those situations force the analyst to rely on creative generation of plausible explanations – ‘storytelling’. We argue that current interaction design approaches obstruct the storytelling process and impede analysts to perform well in their analytical reasoning process. Our Fluidity and Rigour Model combines storytelling with the interaction methods that are needed to support the variety of reasoning and thinking involved in the analytical process. We further contribute with an outline of how the model has informed our designs in the VALCRI project.
Highlights
Analysts in criminal intelligence analysis regularly face data from multiple sources that are often incomplete, possibly deceptive, un-reliable and messy
Intelligence analysts frequently find themselves in situations of high uncertainty and ambiguity
We suggested that current interaction design approaches obstruct the storytelling process and impede analysts in performing well in their analytical reasoning process
Summary
Analysts in criminal intelligence analysis regularly face data from multiple sources that are often incomplete, possibly deceptive, un-reliable and messy This creates situations with high uncertainty and ambiguity, which makes the generation of plausible, reliable arguments difficult or impossible. Many visual analytics and machine learning systems require that data for analysis be available, with the system substituting, for example, system averages for missing data This makes it difficult for analysts to deal with the reality of facing deceptive and missing data. It highlights the variability of analytic reasoning strategies employed by analysts during criminal intelligence and investigative analysis, and describes the range of visualisation and interaction methods needed for criminal intelligence analysis systems. The ease by which a system can be used to express the variability of our thinking processes
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