  Concept bottleneck models perform classification by first predicting which of
a list of human provided concepts are true about a datapoint. Then a downstream
model uses these predicted concept labels to predict the target label. The
predicted concepts act as a rationale for the target prediction. Model trust
issues emerge in this paradigm when soft concept labels are used: it has
previously been observed that extra information about the data distribution
leaks into the concept predictions. In this work we show how Monte-Carlo
Dropout can be used to attain soft concept predictions that do not contain
leaked information.
