  AI and ML models have already found many applications in critical domains,
such as healthcare and criminal justice. However, fully automating such
high-stakes applications can raise ethical or fairness concerns. Instead, in
such cases, humans should be assisted by automated systems so that the two
parties reach a joint decision, stemming out of their interaction. In this work
we conduct an empirical study to identify how uncertainty estimates and model
explanations affect users' reliance, understanding, and trust towards a model,
looking for potential benefits of bringing the two together. Moreover, we seek
to assess how users' behaviour is affected by their own self-confidence in
their abilities to perform a certain task, while we also discuss how the latter
may distort the outcome of an analysis based on agreement and switching
percentages.
