  Methods for interpreting machine learning black-box models increase the
outcomes' transparency and in turn generates insight into the reliability and
fairness of the algorithms. However, the interpretations themselves could
contain significant uncertainty that undermines the trust in the outcomes and
raises concern about the model's reliability. Focusing on the method "Local
Interpretable Model-agnostic Explanations" (LIME), we demonstrate the presence
of two sources of uncertainty, namely the randomness in its sampling procedure
and the variation of interpretation quality across different input data points.
Such uncertainty is present even in models with high training and test
accuracy. We apply LIME to synthetic data and two public data sets, text
classification in 20 Newsgroup and recidivism risk-scoring in COMPAS, to
support our argument.
