  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.
