  We investigate how different active learning (AL) query policies coupled with
classification uncertainty visualizations affect analyst trust in automated
classification systems. A current standard policy for AL is to query the oracle
(e.g., the analyst) to refine labels for datapoints where the classifier has
the highest uncertainty. This is an optimal policy for the automation system as
it yields maximal information gain. However, model-centric policies neglect the
effects of this uncertainty on the human component of the system and the
consequent manner in which the human will interact with the system
post-training. In this paper, we present an empirical study evaluating how AL
query policies and visualizations lending transparency to classification
influence trust in automated classification of image data. We found that query
policy significantly influences an analyst's trust in an image classification
system, and we use these results to propose a set of oracle query policies and
visualizations for use during AL training phases that can influence analyst
trust in classification.
