Artificial intelligence (AI) comes with great opportunities but can also pose
significant risks. Automatically generated explanations for decisions can
increase transparency and foster trust, especially for systems based on
automated predictions by AI models. However, given, e.g., economic incentives
to create dishonest AI, to what extent can we trust explanations? To address
this issue, our work investigates how AI models (i.e., deep learning, and
existing instruments to increase transparency regarding AI decisions) can be
used to create and detect deceptive explanations. As an empirical evaluation,
we focus on text classification and alter the explanations generated by
GradCAM, a well-established explanation technique in neural networks. Then, we
evaluate the effect of deceptive explanations on users in an experiment with
200 participants. Our findings confirm that deceptive explanations can indeed
fool humans. However, one can deploy machine learning (ML) methods to detect
seemingly minor deception attempts with accuracy exceeding 80% given sufficient
domain knowledge. Without domain knowledge, one can still infer inconsistencies
in the explanations in an unsupervised manner, given basic knowledge of the
predictive model under scrutiny.

研究了如何使用深度学习和现有工具来创建和检测欺骗性解释，发现欺骗性解释可以骗过人类，但可以使用机器学习方法检测，即使缺乏领域知识，也可以以无监督的方式推断解释的不一致性。