  Supporting model interpretability for complex phenomena where annotators can
legitimately disagree, such as emotion recognition, is a challenging machine
learning task. In this work, we show that explicitly quantifying the
uncertainty in such settings has interpretability benefits. We use a simple
modification of a classical network inference using Monte Carlo dropout to give
measures of epistemic and aleatoric uncertainty. We identify a significant
correlation between aleatoric uncertainty and human annotator disagreement
($r\approx.3$). Additionally, we demonstrate how difficult and subjective
training samples can be identified using aleatoric uncertainty and how
epistemic uncertainty can reveal data bias that could result in unfair
predictions. We identify the total uncertainty as a suitable surrogate for
model calibration, i.e. the degree we can trust model's predicted confidence.
In addition to explainability benefits, we observe modest performance boosts
from incorporating model uncertainty.
