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.

翻译：本工作的理论基础源于当前人工智能（AI）领域中关于用户信任的论述。我们的目标是提出新颖的人机交互方法，将信任作为促进现有技术采纳（或适应）的推动因素。我们提出了一个框架（HCTFrame），用于指导非专业人士充分挖掘AI设计中用户信任的潜力。通过对三篇文献综述发现进行数据三角验证，本研究澄清了计算机科学与AI论述中关于用户信任的一些误解，并通过三个案例研究评估了心理测量量表在映射潜在用户信任中断与担忧方面的有效性。本工作主要贡献在于对抗设计技术中心式脆弱交互的倾向——这种倾向最终可能导致额外且实际存在的信任缺失。所提出的框架可用于指导系统设计师如何映射和定义用户信任，以及AI系统设计中的社会伦理与组织需求及特征；同时也可指导AI系统设计师如何开发原型并落地满足用户信任需求的解决方案。文章最后提供了若干可衡量用户对拟议解决方案的信任意向与行为的用户研究工具。