  Feature attribution is widely used in interpretable machine learning to
explain how influential each measured input feature value is for an output
inference. However, measurements can be uncertain, and it is unclear how the
awareness of input uncertainty can affect the trust in explanations. We propose
and study two approaches to help users to manage their perception of
uncertainty in a model explanation: 1) transparently show uncertainty in
feature attributions to allow users to reflect on, and 2) suppress attribution
to features with uncertain measurements and shift attribution to other features
by regularizing with an uncertainty penalty. Through simulation experiments,
qualitative interviews, and quantitative user evaluations, we identified the
benefits of moderately suppressing attribution uncertainty, and concerns
regarding showing attribution uncertainty. This work adds to the understanding
of handling and communicating uncertainty for model interpretability.
