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.

翻译：人工智能（AI）系统的适当信任已迅速成为研究人员和实践者关注的重要领域。为实现这一目标，研究者采用了多种方法，例如置信度评分、解释机制、可信度线索或不确定性沟通。然而，由于不同背景衍生出的多元视角以及缺乏对“适当信任”的统一界定，该领域尚缺乏系统性的理解。为探究这一课题，本文通过系统性综述，识别当前构建适当信任的实践方法、测量方式、任务类型及潜在挑战。我们进一步提出信念、意图与行动（BIA）映射框架，通过(a)描述现有关于适当信任定义的争议，以及(b)梳理现有文献中与AI适当信任相关的概念和定义，分析相关概念的共性与差异。最后，本文讨论了在适当信任研究中识别出的挑战，并将观察结果总结为当前趋势、潜在空白及未来研究机遇。总体而言，本文揭示了人机交互中适当信任这一复杂概念的深层内涵，并提出了推动该领域认知发展的研究方向。