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：本研究的理论基础源自当前人工智能（AI）领域关于用户信任的讨论。我们旨在提出新颖的人机交互方法，将信任作为促进当前技术采纳（或适应）的催化剂。我们提出一个框架（HCTFrame），用于指导非专业人士充分挖掘用户信任在人工智能设计中的潜力。通过对三项文献综述发现的数据进行三角验证，所得结果揭示了计算机科学与人工智能领域中关于用户信任的一些误解；同时开展三项案例研究，评估心理测量量表在映射潜在用户信任缺失与担忧方面的有效性。本研究的主要贡献在于对抗技术导向型脆弱交互的设计倾向——此类设计最终可能导致额外且实际存在的信任违背或感知上的信任缺失。所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，以及人工智能系统设计的社会伦理与组织需求特征。该框架还可指导人工智能系统设计者如何开发原型并实施方案，以满足用户信任需求。文章最后提供了一些可用于测量用户对特定方案的信任意图与行为的用户研究工具。