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

Why not both? Complementing explanations with uncertainty, and the role of self-confidence in Human-AI collaboration