Towards Integration of Data for unBiased Intelligence and Trust (TID-BIT)

To expedite 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. We propose the Towards Integration of Data for unBiased Intelligence and Trust (TID-BIT) system for characterizing the sources of error and bias specific to human-generated intelligence. TID-BIT will implement a novel Hierarchical Bayesian Model for high-level situation modeling, threat modeling, and threat prediction that allows the analyst to accurately reuse existing data collected for different intelligence requirements. By quantifying the reliability and credibility of human sources, TID-BIT will be able to estimate and account for uncertainty and bias that impact the high-level fusion process, resulting in improved situational awareness