Reusing information for high-level fusion: characterizing bias and uncertainty in human-generated intelligence

To expedite the intelligence collection process, analysts reuse previously collected data. This poses the risk of analysis failure, because these data are biased in ways that the analyst may not know. Thus, these data may be incomplete, inconsistent or incorrect, have structural gaps and limitations, or simply be too old to accurately represent the current state of the world. Incorporating human-generated intelligence within the high-level fusion process enables the integration of hard (physical sensors) and soft information (human observations) to extend the ability of algorithms to associate and merge disparate pieces of information for a more holistic situational awareness picture. However, in order for high-level fusion systems to manage the uncertainty in soft information, a process needs to be developed for characterizing the sources of error and bias specific to human-generated intelligence and assessing the quality of this data. This paper outlines an approach Towards Integration of Data for unBiased Intelligence and Trust (TID-BIT) that implements a novel Hierarchical Bayesian Model for high-level situation modeling that allows the analyst to accurately reuse existing data collected for different intelligence requirements. TID-BIT constructs situational, semantic knowledge graphs that links the information extracted from unstructured sources to intelligence requirements and performs pattern matching over these attributed-network graphs for integrating information. By quantifying the reliability and credibility of human sources, TID-BIT enables the ability to estimate and account for uncertainty and bias that impact the high-level fusion process, resulting in improved situational awareness.