<p>The integration of Artificial Intelligence (AI) into consumer-facing technologies has raised significant ethical concerns, particularly in relation to user privacy and consent. This study investigates the issue of “invisible consent,” wherein users unknowingly or passively authorize the collection and processing of personal data. A review of 85 documented AI use cases and interviews with stakeholders—including AI developers, privacy experts, and end-users—was conducted across multiple domains such as healthcare, social media, and e-commerce. The findings reveal that despite existing regulatory frameworks like the General Data Protection Regulation (GDPR), current consent mechanisms lack transparency, contextual relevance, and user autonomy. Key ethical principles—including transparency, control, and informed decision-making—are frequently undermined by complex user interfaces, vague legal language, and insufficient user awareness. Building on these findings, this study synthesizes a set of conceptual design principles—transparency, contextual relevance, adaptive control, and explainability—that emerge from the empirical analysis. Rather than advocating for a formal technical framework, the study offers empirically grounded guidelines for developing dynamic, user-centric consent models integrated with explainable AI (XAI). These insights aim to support the development of AI systems that prioritize user trust, ethical compliance, and privacy protection.</p>