# Enhancing User Acceptance of an AI Agent's Recommendation in Information-Sharing Environments
## 1 Introduction
### Background
The potential for modifying users' preferred privacy presence suggests that protecting users' privacy is crucial. This is reflected by extensive regulation, for example, the General Data Protection Regulation (GDPR). GDPR's philosophy is to provide individuals with the ability to control their personal data [33]. Privacy protection, however, is a complicated task due to storage availability (data may exist substantially longer than was intended), data reuse (data may be used for unintended rationales), and data overlap (data about a particular individual may include information about other individuals) [34]. Moreover, obtaining adequate informed consent is a complicated process [28; 35]. Even if people maintain that the protection of their personal data is essential, it does not mean that they are actually attentive to the details of requests for personal data before they consent [36]. Furthermore, current informed consent practices often fail to notify users about the use of personal data properly [27]. Nowadays, with the growth of invasive digital technologies and algorithmic decision making, the challenges of controlling an individual's data have become even more significant [37]. Privacy has been conceptualized as a process of managing interpersonal boundaries with others. In other words, it is a dynamic and ongoing process of setting boundaries between what is being shared and what is withheld [38]. Regulators have made many attempts to solve this problem by helping people protect themselves online. For example, privacy notices and choice, which are essential aspects of privacy and data protection, are regulated worldwide [39]. However, critics argue that, even when a company provides users with real options, they are often ill-equipped to understand and evaluate them. Most people do not read privacy notices [40]. As a result, additional, non-regulatory privacy paradox solutions are being explored, such as privacy awareness [41]. Proposals to improve and fortify notice and consent, such as clearer privacy policies and fairer information practices, will not overcome a fundamental flaw in the model, namely, its assumption that individuals can understand all of the facts relevant to making a proper choice at the moment of pair-wise contracting between individuals and data gatherers [42]. Research also proves that decision making is affected by incomplete and asymmetric information, heuristics and bounded rationality, as well as cognitive and behavioral biases [43]. The human user is helpless when attempting to balance the trade-off between the potential benefits and the resulting costs, mainly due to the lack of technological literacy and cognitive laziness. In some cases, incentives are provided to the user to encourage information sharing [44]. This reality calls for the assistance of an Artificial Intelligence (AI)-based agent. AI and automation can help protect users' privacy online, through smart agents and recommender systems. A Recommendation Agent (RA) is a software agent that elicits the interests or preferences of individual consumers for products, either explicitly or implicitly, and makes recommendations accordingly [45; 46; 47; 48], RAs have the potential to support and improve the quality of the decisions consumers make when searching for and selecting products online. They can reduce the information overload facing consumers, as well as the complexity of online searches. As accurate and \"correct\" as the RA's recommendations might be, an AI agent and/or recommender system cannot be effective unless users are willing to adopt their recommendations, i.e., there must be human-computer trust (HCT) [49]. HCT is defined as the extent to which a user is confident in, and willing to act on the basis of, the recommendations, actions, and decisions of an AI decision aid [49].