# Enhancing User Acceptance of an AI Agent's Recommendation in Information-Sharing Environments
## 4 Discussion
Secondly, AI systems are subjected to bias and fairness issues that must be addressed, leading to the discrimination of different user groups unintentionally [109], or worse, biased toward a pre-determined action intentionally by hostile elements. Thirdly, transparency and explainability in the AI's decision-makingprocesses are essential to gain and maintain user trust and ethical integrity. The field of Explainable Artificial Intelligence (XAI) addresses this specific concern [110]. Lastly, while privacy and data security are crucial needs, the AI's operation often necessitates collecting and analyzing personal data. Therefore, it is essential to ensure that these data are handled with the highest levels of protection and transparency. Users must be fully informed and provide their consent. The ethical issue is being addressed through regulations globally, specifically regarding XAI. For instance, General Data Protection Regulation (GDPR) establishes the \"right for explanation\" [111], detailed in Articles 13-15: \"When an individual is subject to 'a decision based solely on automated processing' that 'produces legal effects'... (or similarly significantly affects)\", GDPR establishes the rights to receive \"meaningful information about the logic involved\". Furthermore, the EU AI Act mandates that high-risk AI systems include a risk management system ensuring safety and compliance (Article 9). It also requires that these systems offer sufficient transparency for users to understand and utilize their outputs effectively (Article 13) and mechanisms to allow humans to oversee the process during its use (Article 14). Providers must inform users when interacting with AI, with exceptions for specific law enforcement scenarios (Article 52). These provisions promote safe, transparent, and accountable AI use across various applications [112]. The AI Bill of Rights [108], published by the U.S. government, articulates key principles to guide the development and use of AI. It emphasizes transparency as crucial, as users must understand how AI systems operate and make decisions. In particular, Artificial Intelligence systems must facilitate third-party evaluations, deliver explicit notifications to users, and furnish succinct, tailored explanations of their decisions. These explanations should be understandable to the intended audience, commensurate with the associated risks, and be accurate. Implementing these measures ensures transparency, accountability, and user-friendliness in AI operations. Paradoxically, applying sophisticated UI tools may enhance the user's trust in AI agents; however, if the agent is not trusted, it may harm the user. Therefore, the above principles and regulations are mandatory. Unlike the AI core problem, which can be solved almost purely mathematically, the UI problem mainly involves soft human factors that are difficult to predict [113]. Previous studies have tested several factors influencing the adoption of AI recommendations and human-AI trust. For example, different visualizations have been proven to impact this trust through increased confidence and cognitive fit by comparing three AI decision visualization attribution models [114]. However, as many others, the latter research deals with an isolated UI component and not its possible combinations. Another study looked into AI assistants in tourism and found that the predictors of chatbot adoption intention are perceived ease of use, perceived usefulness, perceived trust, perceived intelligence, and anthropomorphism [115]. While it supports the necessity of the current research's objective, it does not provide the specific methodology. There are a variety of factors that influence human decision makers in AI environments, e.g., the influences of perceived costs and perceived benefits on an AI-driven interactive recommendation agent were tested by examining its influence during a consumers' decision-making process [116].