Societal Implications of Community-Oriented Policing and Technology

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. However, 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. Failures in the assessment of criminal situations or the inability to come to a conclusion as the result of an analytical process can lead to severe consequences. A lack of awareness, overlooking or not realising the need to locate a key piece of information because one does not know the data exist can also lead to human errors. One solution to this problem is the facilitation of storytelling. Storytelling requires data to be assembled and organised to tell a story that explains a situation or phenomenon. By externalising and making the storytelling process visible and tangible to the analyst via a computer display, it becomes possible for the analyst to inspect his or her own reasoning processes. This creates the possibility to check one’s analyses and assumptions for omissions and contradictions. Analysts need a kind of user interface that allows them to easily explore different ways to organise and sequence existing data into plausible stories or explanations that can eventually evolve into narratives that bind the data together into a formal explanation. If an analyst is presented with limited data or even no data, then such a tool must allow the analyst to easily make assumptions and suppositions that could be used to initiate a line of inquiry or connect separate pieces of data to concoct a plausible explanation.