Assessing User Trust in Active Learning Systems: Insights from Query Policy and Uncertainty Visualization
Ian Thomas, Song Young Oh, Danielle Albers Szafir · 2024
Active learning (AL) 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 presents a significant obstacle as analyst over- or underreliance may adversely affect a given application. Common AL strategies enhance classification models by asking analysts to provide labels for data points with the highest degree of uncertainty. However, such 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. We present an empirical study assessing how AL query policies and visualizations that enhance transparency in a classifier’s decisions influence trust in automated image classifiers. We found that query policy may significantly influence an analyst’s perception of system capabilities, while the level of visual transparency into classifier certainty may influence an analyst’s ability to perform a classification task. Our study informs the design of interactive labeling systems to help mitigate the effects of overreliance and calibrate appropriate trust in automated systems.