Information-sharing (IS) occurs in almost every action of our daily life. IS holds benefits for its users, but it is also a source of privacy violations. Human users struggle to balance this trade-off between the potential benefits and the resulting costs. This reality calls for Artificial-Intelligence (AI)-based agent assistance that surpasses humans’ bottom-line utility, as shown in previous research. However, convincing an individual to follow an AI agent’s recommendation is not trivial; therefore, the current research goal is establishing trust in the machine. To this end, based on the Design of Experiments (DOE) approach, we developed a methodology that optimizes the user-interface (UI) with a target function of maximizing the AI agent recommendation acceptance. To empirically demonstrate our methodology, we conducted an experiment with eight UI factors and (n=64) human participants acting in a Facebook simulator environment accompanied by an AI-agent assistant. Based on the results, we showed how the methodology can be implemented to optimize the agent’s users’ acceptance. Finally, while our methodology was tested empirically on an IS platform, it could be applied straightforwardly in other domains.