Interpreting Predictive Probabilities: Model Confidence or Human Label
  Variation?
Joris Baan Raquel Fernández Barbara Plank Wilker Aziz
With the rise of increasingly powerful and user-facing NLP systems, there is
growing interest in assessing whether they have a good representation of
uncertainty by evaluating the quality of their predictive distribution over
outcomes. We identify two main perspectives that drive starkly different
evaluation protocols. The first treats predictive probability as an indication
of model confidence; the second as an indication of human label variation. We
discuss their merits and limitations, and take the position that both are
crucial for trustworthy and fair NLP systems, but that exploiting a single
predictive distribution is limiting. We recommend tools and highlight exciting
directions towards models with disentangled representations of uncertainty
about predictions and uncertainty about human labels.