  Evaluating the efficiency of human-AI interactions is challenging, including
subjective and objective quality aspects. With the focus on the human
experience of the explanations, evaluations of explanation methods have become
mostly subjective, making comparative evaluations almost impossible and highly
linked to the individual user. However, it is commonly agreed that one aspect
of explanation quality is how effectively the user can detect if the
predictions are trustworthy and correct, i.e., if the explanations can increase
the user's appropriate trust in the model. This paper starts with the
definitions of appropriate trust from the literature. It compares the
definitions with model performance evaluation, showing the strong similarities
between appropriate trust and model performance evaluation. The paper's main
contribution is a novel approach to evaluating appropriate trust by taking
advantage of the likenesses between definitions. The paper offers several
straightforward evaluation methods for different aspects of user performance,
including suggesting a method for measuring uncertainty and appropriate trust
in regression.
