CSRI’s Federico Cerutti presented the paper “A Tool to Highlight Weaknesses and Strengthen Cases: CISpaces.org”, co-authored by Timothy Norman and Alice, at ‘JURIX 2018’. JURIX is the 31st international conference on Legal Knowledge and Information Systems, hosted by the Faculty of Law and the department of Artificial Intelligence in the Bernoulli Institute of Mathematics, Computer Science and Artificial Intelligence, Faculty of Science and Engineering of the University of Groningen. Federico presented in the session on Legal Reasoning and Argumentation: Theoretical and Practice Experiences.
In the paper, the authors demonstrate CISpaces.org, a tool to support situational understanding in intelligence analysis that complements but not replaces human expertise, for the first time applied to a judicial context. The system combines argumentation-based reasoning and natural language generation to support the creation of analysis and summary reports, and to record the process of forming hypotheses from relationships among information.
CISpaces.org supports the data-to-decision process, from hypotheses formation, to report generation that can be used for briefings to inform legal practitioners, or even judges. CISpaces.org facilitates sensemaking in a declarative format. Differently from existing tools [7,6], CISpaces.org provides a method to record and support the process of forming hypotheses from the relationships among information which enables the analyst to highlight information or assumptions that may lead to interrelated as well as alternative hypotheses. CISpaces.org makes this core process of reasoning explicit, providing further support for structuring reasoning and mitigating biases. The reasoning mechanism identifies what evidence and claims together constitute a plausible interpretation of an analysis.
CISpaces.org is freely available for being used at http://tiny.cc/CISpaces (username: demo, password: demo), and it can be downloaded at GitHub, https://github.com/CISpaces, with MIT license.
You can access the paper via: http://orca.cf.ac.uk/id/eprint/117478
This work is part of the DAIS ITA project at CSRI.