  Attention based explanations (viz. saliency maps), by providing
interpretability to black box models such as deep neural networks, are assumed
to improve human trust and reliance in the underlying models. Recently, it has
been shown that attention weights are frequently uncorrelated with
gradient-based measures of feature importance. Motivated by this, we ask a
follow-up question: "Assuming that we only consider the tasks where attention
weights correlate well with feature importance, how effective are these
attention based explanations in increasing human trust and reliance in the
underlying models?". In other words, can we use attention as an explanation? We
perform extensive human study experiments that aim to qualitatively and
quantitatively assess the degree to which attention based explanations are
suitable in increasing human trust and reliance. Our experiment results show
that attention cannot be used as an explanation.
