  A current concern in the field of Artificial Intelligence (AI) is to ensure
the trustworthiness of AI systems. The development of explainability methods is
one prominent way to address this, which has often resulted in the assumption
that the use of explainability will lead to an increase in the trust of users
and wider society. However, the dynamics between explainability and trust are
not well established and empirical investigations of their relation remain
mixed or inconclusive. In this paper we provide a detailed description of the
concepts of user trust and distrust in AI and their relation to appropriate
reliance. For that we draw from the fields of machine learning, human-computer
interaction, and the social sciences. Furthermore, we have created a survey of
existing empirical studies that investigate the effects of AI systems and XAI
methods on user (dis)trust. With clarifying the concepts and summarizing the
empirical investigations, we aim to provide researchers, who examine user trust
in AI, with an improved starting point for developing user studies to measure
and evaluate the user's attitude towards and reliance on AI systems.
