Here is the abstract:
Criminal investigation is a difficult and laborious process that is prone to error as teams of investigators may be subject to tunnel vision, groupthink, and confirmation bias. As a result, miscarriages of justice may ensue. To overcome these problems, in the Dutch law enforcement organization, crime analysts have been given a more important role. It is now their task to critically evaluate the investigation that is going on. They have to make sense of the vast amount of evidence available in a case by generating plausible scenarios about what might have happened. Subsequently, they have to assess the quality of their scenarios and choose the best alternative. Due to the difficulty of this process, a great need exists for software that supports crime analysts in their task. However, current support tools for crime analysis do not allow analysts to record scenarios and their relation to the evidence and as a result the most important part of the analysis process remains in the analysts’ minds. Therefore, they may benefit from so-called sensemaking systems that allow them to make their reasoning process explicit by visualizing scenarios and the reasons why these scenarios are supported by the evidence. Nevertheless, such sensemaking tools for crime analysis are relatively sparse and often do not incorporate a logical model of reasoning with evidence in the context of crime analysis. This thesis aims to fill this gap by proposing sensemaking software that has specifically been designed for crime analysis. Such a tool should be rationally well-founded, natural, useful, usable, and effective. To this aid, a proof-of-concept application called AVERs (Argument Visualization for Evidential Reasoning based on stories) was built that implements a rationally well-founded and natural model of the reasoning that takes place in crime analysis. In this way a standard of rational reasoning is encouraged and errors may be reduced. Using AVERs analysts are able to create visual representations of scenarios and evidential arguments. Scenarios are represented as causal networks of events, while evidential arguments are arguments based on the evidential data in the case. Such arguments are based on argumentation schemes that often come with critical questions. These questions make the analysts more aware of possible sources of doubt and encourage them to critically examine the evidence. Evidential arguments can be used to support or attack scenarios with the available evidence. In this way, this software allows the analysts to reason about scenarios and to critically evaluate them. Moreover, it provides features that can be used to compare alternative scenarios. A series of empirical studies has confirmed that the design and implementation of AVERs fulfills all five criteria to a certain degree. This means that it is useful to crime analysts and satisfies their desires, while it may improve their analysis of the case and the communication of their results to the investigators working on the case, and ensures that rational analyses are performed. Therefore, through this software in the future biases in the crime analysis process may be avoided.
Tags: Criminal investigation, Legal argument, Legal argumentation, Legal informatics dissertations, Legal informatics theses, Legal decision support systems, Artificial intelligence and law, Legal argumentation systems, Visualization of legal information, Legal reasoning, Legal evidence information systems, Criminal justice information systems, Criminal law information systems, Criminal investigation information systems, Legal communication, Modeling legal argumentation, Narrative in legal evidence, Narrative in criminal law, Empirical methods in legal informatics, AVERS, Narrative based legal reasoning, Legal logic, Modeling legal reasoning, Modeling legal argument, Empirical methods in legal communication studies, Legal expert systems, Sensemaking systems, Legal sensemaking systems, Crime analysis systems, Crime analysis expert systems, Crime analysis decision support systems, Argument Visualization for Evidential Reasoning based on stories, Legal evidentiary reasoning, Modeling legal evidentiary reasoning, Susan van den Braak, Universiteit Utrecht Department of Information and Computing Sciences, Henry Praaken, Legal information visualization tools