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A current concern in the field of Artificial Intelligence (AI) is to ensurethe trustworthiness of AI systems. The development of explainability methods isone prominent way to address this, which has often resulted in the assumptionthat the use of explainability will lead to an increase in the trust of usersand wider society. However, the dynamics between explainability and trust arenot well established and empirical investigations of their relation remainmixed or inconclusive. In this paper we provide a detailed description of theconcepts of user trust and distrust in AI and their relation to appropriatereliance. For that we draw from the fields of machine learning, human-computerinteraction, and the social sciences. Furthermore, we have created a survey ofexisting empirical studies that investigate the effects of AI systems and XAImethods on user (dis)trust. With clarifying the concepts and summarizing theempirical investigations, we aim to provide researchers, who examine user trustin AI, with an improved starting point for developing user studies to measureand evaluate the user's attitude towards and reliance on AI systems.

Trust, distrust, and appropriate reliance in (X)AI: a survey of empirical evaluation of user trust