Assessing User Trust in Active Learning Systems: Insights from Query Policy and Uncertainty Visualization

Active learning systems have become increasingly popular for various applications in machine learning (ML), including medical imaging, environmental monitoring, and geospatial analysis. These systems rely on inputs dynamically queried from people to enhance classification. Ensuring appropriate analyst trust in these systems remains a significant obstacle, as analysts may over-rely or under-rely on the system. Common active learning (AL) strategies enhance classification models by asking an analyst to provide labels for data points with the highest degree of uncertainty. However, model-centric policies do not consider potential priming effects on the analyst and how they will affect people's trust in the system post-training. In this paper, we present an empirical study assessing how AL query policies and visualizations that enhance transparency in a classifier’s certainty influence trust in automated image classifiers. We found that query policy may significantly influence an analyst’s perception of the system’s capabilities, while the level of visual transparency into classifier certainty may influence an analyst’s ability to perform the classification task. Our study informs the design of interactive labeling systems to help mitigate the effects of over-reliance.