  As AI technology is increasingly applied to high-impact, high-risk domains,
there have been a number of new methods aimed at making AI models more human
interpretable. Despite the recent growth of interpretability work, there is a
lack of systematic evaluation of proposed techniques. In this work, we
introduce HIVE (Human Interpretability of Visual Explanations), a novel human
evaluation framework that assesses the utility of explanations to human users
in AI-assisted decision making scenarios, and enables falsifiable hypothesis
testing, cross-method comparison, and human-centered evaluation of visual
interpretability methods. To the best of our knowledge, this is the first work
of its kind. Using HIVE, we conduct IRB-approved human studies with nearly 1000
participants and evaluate four methods that represent the diversity of computer
vision interpretability works: GradCAM, BagNet, ProtoPNet, and ProtoTree. Our
results suggest that explanations engender human trust, even for incorrect
predictions, yet are not distinct enough for users to distinguish between
correct and incorrect predictions. We open-source HIVE to enable future studies
and encourage more human-centered approaches to interpretability research.
