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。

促进人工智能与人类互动中适当信任的系统性综述

A Systematic Review on Fostering Appropriate Trust in Human-AI  Interaction

As automated machine learning (AutoML) systems continue to progress in both
sophistication and performance, it becomes important to understand the `how'
and `why' of human-computer interaction (HCI) within these frameworks, both
current and expected. Such a discussion is necessary for optimal system design,
leveraging advanced data-processing capabilities to support decision-making
involving humans, but it is also key to identifying the opportunities and risks
presented by ever-increasing levels of machine autonomy. Within this context,
we focus on the following questions: (i) How does HCI currently look like for
state-of-the-art AutoML algorithms, especially during the stages of
development, deployment, and maintenance? (ii) Do the expectations of HCI
within AutoML frameworks vary for different types of users and stakeholders?
(iii) How can HCI be managed so that AutoML solutions acquire human trust and
broad acceptance? (iv) As AutoML systems become more autonomous and capable of
learning from complex open-ended environments, will the fundamental nature of
HCI evolve? To consider these questions, we project existing literature in HCI
into the space of AutoML; this connection has, to date, largely been
unexplored. In so doing, we review topics including user-interface design,
human-bias mitigation, and trust in artificial intelligence (AI). Additionally,
to rigorously gauge the future of HCI, we contemplate how AutoML may manifest
in effectively open-ended environments. This discussion necessarily reviews
projected developmental pathways for AutoML, such as the incorporation of
reasoning, although the focus remains on how and why HCI may occur in such a
framework rather than on any implementational details. Ultimately, this review
serves to identify key research directions aimed at better facilitating the
roles and modes of human interactions with both current and future AutoML
systems.

本文主要探讨在当前和未来的自动机器学习系统中，人与计算机交互如何发生（包括开发、部署和维护阶段的 HCI），不同类型的用户和利益相关方对 HCI 的期望是否存在差异，如何管理 HCI 以使自动机器学习获得人的信任和广泛接受，以及随着自动机器学习系统变得更加自主和学习能力增强，HCI 的基本特性是否会发生变化。研究关键领域包括自动化机器学习，人机交互，用户界面设计，人工智能信任等方面。