Resumen
Vehicles and platforms with multiple sensors connect people in multiple roles with different responsibilities to scenes of interest. For many of these human?sensor systems there are a variety of algorithms that transform, select, and filter the sensor data prior to human intervention. Emergency response, precision agriculture, and intelligence, surveillance and reconnaissance (ISR) are examples of these human?computation?sensor systems. The authors examined a case of the latter to understand how people in various roles utilize the algorithms output to identify meaningful properties in data streams given uncertainty. The investigations revealed: (a) that increasingly complex interactions occur across agents in the human?computation?sensor system; and (b) analysts struggling to interpret the output of ?black box? algorithms given uncertainty and change in the scenes of interest. The paper presents a new interactive visualization concept designed to ?open up the black box? of sensor processing algorithms to support human analysts as they look for meaning in feeds from sensors.