We present a randomized controlled trial for a model-in-the-loop regression
task, with the goal of measuring the extent to which (1) good explanations of
model predictions increase human accuracy, and (2) faulty explanations decrease
human trust in the model. We study explanations based on visual saliency in an
image-based age prediction task for which humans and learned models are
individually capable but not highly proficient and frequently disagree. Our
experimental design separates model quality from explanation quality, and makes
it possible to compare treatments involving a variety of explanations of
varying levels of quality. We find that presenting model predictions improves
human accuracy. However, visual explanations of various kinds fail to
significantly alter human accuracy or trust in the model - regardless of
whether explanations characterize an accurate model, an inaccurate one, or are
generated randomly and independently of the input image. These findings suggest
the need for greater evaluation of explanations in downstream decision making
tasks, better design-based tools for presenting explanations to users, and
better approaches for generating explanations.

本试验的目的是研究在模型与人类在图像年龄预测任务中表现相似情况下，给人类提供好的模型预测解释是否会提高人类预测的准确度，而不好的解释会降低人类对模型的信任。研究发现，给人类提供模型预测能提高人类准确度，但图像解释并不能改变人类对模型的准确度或信任的显著差异。这一发现表明有必要对后续的决策任务进行更全面的解释评估，为用户提供基于设计的解释工具，并思考更好的生成解释的方法。