  Explainable machine learning and artificial intelligence models have been
used to justify a model's decision-making process. This added transparency aims
to help improve user performance and understanding of the underlying model.
However, in practice, explainable systems face many open questions and
challenges. Specifically, designers might reduce the complexity of deep
learning models in order to provide interpretability. The explanations
generated by these simplified models, however, might not accurately justify and
be truthful to the model. This can further add confusion to the users as they
might not find the explanations meaningful with respect to the model
predictions. Understanding how these explanations affect user behavior is an
ongoing challenge. In this paper, we explore how explanation veracity affects
user performance and agreement in intelligent systems. Through a controlled
user study with an explainable activity recognition system, we compare
variations in explanation veracity for a video review and querying task. The
results suggest that low veracity explanations significantly decrease user
performance and agreement compared to both accurate explanations and a system
without explanations. These findings demonstrate the importance of accurate and
understandable explanations and caution that poor explanations can sometimes be
worse than no explanations with respect to their effect on user performance and
reliance on an AI system.
