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We present a randomized controlled trial for a model-in-the-loop regressiontask, with the goal of measuring the extent to which (1) good explanations ofmodel predictions increase human accuracy, and (2) faulty explanations decreasehuman trust in the model. We study explanations based on visual saliency in animage-based age prediction task for which humans and learned models areindividually capable but not highly proficient and frequently disagree. Ourexperimental design separates model quality from explanation quality, and makesit possible to compare treatments involving a variety of explanations ofvarying levels of quality. We find that presenting model predictions improveshuman accuracy. However, visual explanations of various kinds fail tosignificantly alter human accuracy or trust in the model - regardless ofwhether explanations characterize an accurate model, an inaccurate one, or aregenerated randomly and independently of the input image. These findings suggestthe need for greater evaluation of explanations in downstream decision makingtasks, better design-based tools for presenting explanations to users, andbetter approaches for generating explanations.

Are Visual Explanations Useful? A Case Study in Model-in-the-Loop Prediction