When collaborating with an AI system, we must assess when to trust its recommendations. Suppose we mistakenly trust it in regions where it is likely to err. In that case, catastrophic failures may occur, hence the need for Bayesian approaches for reasoning and learning to determine the confidence (or epistemic uncertainty) in the probabilities of the queried outcome. Pure Bayesian methods, however, suffer from high computational costs. To overcome them, we revert to efficient and effective approximations. This talk will discuss some techniques that take the name of evidential reasoning and learning, from the Bayesian update of given hypotheses based on additional evidence collected. We will discuss probabilistic circuits with uncertain probabilities, uncertainty-aware deep classifiers, and current research directions in neuro-symbolic settings, including a recent application to passive radars.