In this paper, we report on research underpinning CISpaces.org, a tool to support the process of sense-making, complementing human expertise in the generation of intelligence products. The model combines a structured, graphical representation of the analyst’s reasoning process with efficient artificial intelligence algorithms to automatically identify plausible hypotheses. Information extracted from open sources can be exploited in the sense-making process, and analysts may collaborate to bring different perspectives to the problem concerned. The provenance of both evidence used and analyses (co-)produced are recorded and may be used for further investigation, reporting and audit.
The methodology provides a rigorous means to record and support the process of forming hypotheses from the relationships among information. We use natural language processing algorithms to extract factual claims from open information sources. The core process of reasoning is made explicit in the structuring of evidence. Given this, we do not rely on the analyst exhaustively enumerating all possible hypotheses; we automate the identification of what evidence and claims together constitute a plausible interpretation of an analysis, enabling the analyst may explore all possibilities. As a further means to mitigate biases in human reasoning, we highlight critical questions that may undermine inferential assumptions of various kinds: Is there an alternative cause? Do other experts disagree? These structured models may then be used to automatically generate tailored reports to key decision makers as required, or as the situational understanding shifts.
Timothy J. Norman, Federico Cerutti, Stuart Middleton, and Alice Toniolo, "Argumentation-based sense-making exploiting open sources (Conference Presentation)," Proc. SPIE 10653, Next-Generation Analyst VI, 1065309 (Presented at SPIE Defense + Security: April 16, 2018; Published: 14 May 2018); https://doi.org/10.1117/12.2306217.5783307409001.
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