# Enhancing User Acceptance of an AI Agent's Recommendation in Information-Sharing Environments
## 4 Discussion  
In this study, we introduced a methodology to optimize a user interface (UI) to effectively convey the output of an Artificial Intelligence (AI)-based agent to the user, aimed at achieving a high acceptance rate of the recommendation. The AI agent's purpose was to assist the user with decision making while sharing information. In previous research, it was proven empirically that the AI agent does this significantly better than a human agent [19]. The challenge, however, is to convince users to adopt the AI agent's recommendation; in other words, to gain the user's trust [104]. The methodology is based on designing an experiment that estimates the effect of various combinations of UI tools (factors) on the user adoption rate, including the main effects and a chosen order of interactions between the UI tools. We demonstrated the application of this methodology empirically in a simulated Facebook environment, where the AI agent made recommendations to the participants (each one separately) regarding their information-sharing decisions. The empirical study included eight UI tools (experimental factors) and considered the main effects and two-level interactions. Using a series of 16 successful experiments, with \(n=64\) participants, we showed how the optimal interface could be determined. It can be seen that the UIUX design is the only significant main effect. This is probably due to the fact that it is the simplest to understand. In contrast, all other factors required more effort in reading and understanding the text or number's scale and meaning, which requires more concentration and is less intuitive. Also, factors like the provider are not clear-cut for users, choosing between trusting EU and FB. However, the causality of the factor effect is not part of this research's scope and can be further researched. As the sample size of 64 participants (and experiments) is relatively small and is the minimal one that satisfies the results' resolution requirement according to the factorial design, we recommend increasing the sample size in future research to achieve more robust and significant results. The core experiment design can be preserved; however, repetition may assist in achieving the above goals. AI tools are becoming increasingly prevalent and are the major driving force behind the fourth industrial revolution [105]. However, most systems are not fully automated, and AI technology is often implemented as a set of user recommendations. In this reality, trust in the AI agent, and the resultant recommendation adoption rate, is a significant issue [68]. In some cases, the challenge of convincing the user to adopt the recommendations may even be more challenging than calculating the best action (the AI core). The deployment of tools to enhance adoption of AI agents that may influence user behavior raises significant ethical considerations that must be addressed. First and foremost, the potential of AI to manipulate a user's behavior can undermine freedom of opinions and freedom of choice. Specifically, user autonomy is at risk, as it may lead to decisions that are not in the user's best interest or that are harmful, as revealed in the study by [PERSON] et al. [106], where a machine learning model trained on pneumonia patients mistakenly classified those with asthma as lower-risk patients )because in the training dataset typically they immediately received intensive care). Therefore, if used blindly, the model could risk lives by not admitting asthma patients who need critical treatment. There are significant concerns about AI being exploited for manipulative or harmful practices and using it blindly [107]. Thus, ensuring that AI agents respect user autonomy and avoid manipulative practices are critical [108]. This can be achieved by enabling the user to make the final judgment at each decision node.