Appropriate Trust in Artificial Intelligence (AI) systems has rapidly become
an important area of focus for both researchers and practitioners. Various
approaches have been used to achieve it, such as confidence scores,
explanations, trustworthiness cues, or uncertainty communication. However, a
comprehensive understanding of the field is lacking due to the diversity of
perspectives arising from various backgrounds that influence it and the lack of
a single definition for appropriate trust. To investigate this topic, this
paper presents a systematic review to identify current practices in building
appropriate trust, different ways to measure it, types of tasks used, and
potential challenges associated with it. We also propose a Belief, Intentions,
and Actions (BIA) mapping to study commonalities and differences in the
concepts related to appropriate trust by (a) describing the existing
disagreements on defining appropriate trust, and (b) providing an overview of
the concepts and definitions related to appropriate trust in AI from the
existing literature. Finally, the challenges identified in studying appropriate
trust are discussed, and observations are summarized as current trends,
potential gaps, and research opportunities for future work. Overall, the paper
provides insights into the complex concept of appropriate trust in human-AI
interaction and presents research opportunities to advance our understanding on
this topic.

这篇论文通过系统综述研究了人工智能系统中适当信任的建立、度量方式、使用的任务类型以及相关挑战，并提出了一种基于信仰、意图和行为的映射方法，总结了当前趋势、潜在差距和未来工作的研究机会，以推进我们对适当信任的复杂概念在人工智能交互中的理解。