A Study on Multimodal and Interactive Explanations for Visual Question
  Answering
  Explainability and interpretability of AI models is an essential factor
affecting the safety of AI. While various explainable AI (XAI) approaches aim
at mitigating the lack of transparency in deep networks, the evidence of the
effectiveness of these approaches in improving usability, trust, and
understanding of AI systems are still missing. We evaluate multimodal
explanations in the setting of a Visual Question Answering (VQA) task, by
asking users to predict the response accuracy of a VQA agent with and without
explanations. We use between-subjects and within-subjects experiments to probe
explanation effectiveness in terms of improving user prediction accuracy,
confidence, and reliance, among other factors. The results indicate that the
explanations help improve human prediction accuracy, especially in trials when
the VQA system's answer is inaccurate. Furthermore, we introduce active
attention, a novel method for evaluating causal attentional effects through
intervention by editing attention maps. User explanation ratings are strongly
correlated with human prediction accuracy and suggest the efficacy of these
explanations in human-machine AI collaboration tasks.
