论文标题
机器生成的解释对普通用户的用处有多有用？人类对猜测错误预测的标签的评估
How Useful Are the Machine-Generated Interpretations to General Users? A Human Evaluation on Guessing the Incorrectly Predicted Labels
论文作者
论文摘要
向用户解释为什么自动化系统犯某些错误是重要且具有挑战性的。研究人员提出了自动为深神经网络模型产生解释的方法。但是，尚不清楚这些解释在帮助用户弄清为什么会遇到错误时有多么有用。如果一种解释有效地向用户解释了潜在的深层神经网络模型如何工作，那么与不相比，使用该解释的人应该更好地预测模型的输出。本文提出了一项调查，以了解是否显示机器生成的视觉解释有助于用户了解图像分类器产生的错误预测标签。我们向150名在线人群工人展示了图像和正确的标签，并要求他们选择错误的预测标签，无论有或不向他们显示机器生成的视觉解释。结果表明，显示视觉解释并没有增加，而是减少了平均猜测精度约为10％。
Explaining to users why automated systems make certain mistakes is important and challenging. Researchers have proposed ways to automatically produce interpretations for deep neural network models. However, it is unclear how useful these interpretations are in helping users figure out why they are getting an error. If an interpretation effectively explains to users how the underlying deep neural network model works, people who were presented with the interpretation should be better at predicting the model's outputs than those who were not. This paper presents an investigation on whether or not showing machine-generated visual interpretations helps users understand the incorrectly predicted labels produced by image classifiers. We showed the images and the correct labels to 150 online crowd workers and asked them to select the incorrectly predicted labels with or without showing them the machine-generated visual interpretations. The results demonstrated that displaying the visual interpretations did not increase, but rather decreased, the average guessing accuracy by roughly 10%.
