Interface Design and Task Difficulty Impact ML-Assisted Visual Data Foraging

Data foraging routinely involves sifting through a large amount of irrelevant information in search of relevant data. In machine learning, the related task of active search considers the automated discovery of rare, valuable items from large data sets -- a setting that maps directly onto data foraging. Although there has been a long history of integrating similar assistive technologies into the visual analytics pipeline, we do not fully understand how these technologies impact human behavior or what factors might impact the machine partners' effectiveness. We frame data foraging as a sequential decision-making process and propose using active search as an assistive technology for accelerating discovery. We conduct a crowd-sourced user study to evaluate this human-machine partnership in data foraging and show that our approach results in higher throughput and more meaningful interactions during interactive visual exploration and discovery. Furthermore, we present evidence from a follow-up user study that the impact of incorporating assistive technology in visual tasks varies with interface design and task difficulty.