The rationale of this work is based on the current user trust discourse of
Artificial Intelligence (AI). We aim to produce novel HCI approaches that use
trust as a facilitator for the uptake (or appropriation) of current
technologies. We propose a framework (HCTFrame) to guide non-experts to unlock
the full potential of user trust in AI design. Results derived from a data
triangulation of findings from three literature reviews demystify some
misconceptions of user trust in computer science and AI discourse, and three
case studies are conducted to assess the effectiveness of a psychometric scale
in mapping potential users' trust breakdowns and concerns. This work primarily
contributes to the fight against the tendency to design technical-centered
vulnerable interactions, which can eventually lead to additional real and
perceived breaches of trust. The proposed framework can be used to guide system
designers on how to map and define user trust and the socioethical and
organisational needs and characteristics of AI system design. It can also guide
AI system designers on how to develop a prototype and operationalise a solution
that meets user trust requirements. The article ends by providing some user
research tools that can be employed to measure users' trust intentions and
behaviours towards a proposed solution.

该论文的工作基于当前人工智能用户信任话语，旨在通过使用信任作为促进当前技术采用（或适用）的工具，提出一种新颖的人机交互方法。作者提出了一个框架（HCTFrame），以指导非专家解锁用户对 AI 设计的信任的全部潜力。经过三个文献综述发现的数据三角化得出的结果揭示了计算机科学和人工智能话语中有关用户信任的一些误解，并进行了三个案例研究评估了心理测量量表的有效性，以映射潜在用户的信任破裂和关注点。该工作主要对抗设计技术中心易受攻击的交互的趋势，这最终可能导致更多现实和感知的信任违规。提出的框架可用于指导系统设计者，如何映射和定义用户信任，以及 AI 系统设计的社会伦理和组织需求和特征。它还可以指导 AI 系统设计者如何开发原型并使解决方案实现用户信任要求。该文章最后提供了一些用户研究工具，可用于测量用户的信任意向和行为，以评估所提出的解决方案。