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Current intelligence, surveillance, and reconnaissance (ISR) practices are limited in their ability to address the complexity and ambiguity of urban environments. The complexity is a function of dense... Current intelligence, surveillance, and reconnaissance (ISR) practices are limited in their ability to address the complexity and ambiguity of urban environments. The complexity is a function of densely populated, densely structured, diverse, and heterogeneous terrain. The ambiguity is due to adversaries blending in among civilian populations and masking acts of planning or executing hostilities within the patterns of everyday urban life. Advances in artificial intelligence (AI) technologies, such as machine vision, show promise towards aiding humans in negotiating this complexity and ambiguity. Yet the dimensionality of the inference problem is too high and the base rate of threats too infrequent for these tools to accurately distinguish among threats and non-threats based solely on passively observed behaviors. As such, human-based inference – replete with cognitive bias and imperfect memory – remains our most reliable method for determining who may be a threat and who is merely going about their day.