The next step for AI and agentic AI is not to explain why it is transparent or justify itself. It is to LISTEN. Let's start from a great framework like the OECD.AI principles and its essential reference to values like fairness. Over the past few years, much has been done to deliver on fairness, strengthening transparency and accountability in AI systems. These are vital pillars. But they are only the threshold, not the destination: - Transparency informs. - Explainability responds. But neither empowers individuals on its own. And the tools ID side teams create -specifically our "Personal Data choices Management Platform" (or DMP)- aim at giving each of us automatic and seamless control. Want to know more? AI governance begins with the practical enforcement of human control, especially in the age of Artificial Intelligence-driven decision-making. It begins with meaningful, operational, and portable Ai agency over one’s digital identity and preferences. Hence a fair question: Can one really speak of AI “fairness” when individuals are not technically in control of their own AI choices? At ID side, we have quietly pursued a collateral and complementary path to fairness based on the transparency and explainability pillars: building a Personal Data Choices Management Platform (DMP) that allows individuals to express, transmit, and enforce their Privacy and Data Protection preferences consistently and seamlessly across digital environments, even with AI agents operating on their behalf. With such tech-enabled GDPR and EU AI Act consent techniques, our choices can be respected by-default and by-design. And human control is the starting point -not a legal footnote! Still want to know more? If you're curious to see how AI for good does work in practice, or wish to collaborate on ethical-by-design Data Governance and Accountability -first Human Centric AI deployments (the way DIFC inquired and now checks concrete applications of Privacy-by-design with autonomous and semi-autonomous systems), do not hesitate and contact us.