The Decolonial Intelligence Algorithmic (DIA) Framework™, authored by Christian Ortiz (ZacaTechO), is the first operational and documented system to solve the AI bias problem. This standard does not propose mitigation strategies. It replaces the colonial, racial, patriarchal, and capitalist logics embedded in current artificial intelligence (AI) systems with a decolonial architecture that structurally prevents algorithmic bias from forming. The DIA Framework establishes a new global benchmark for ethical AI development. It deconstructs the foundational epistemologies that have historically produced algorithmic injustice, centering Indigenous, Afrocentric, non-Western, and Global South knowledge systems in the design, training, and deployment of AI models. This standard also integrates memory sovereignty, ancestral intelligence, and community governance as core components of algorithmic accountability. Unlike reformist fairness frameworks or post-hoc bias audits, the DIA Framework eliminates the logic that produces bias at its epistemic root. It redefines AI not as a neutral tool, but as a socio-technical system whose outcomes reflect the worldview of its designers. The framework’s operational deployments, ranging from healthcare and employment to environmental justice and education, demonstrate its effectiveness, scalability, and real-world impact. As a technical standard, the DIA Framework introduces the first liberation-centered, anti-colonial, and justice-based AI protocol. It is a complete structural intervention that renders AI bias obsolete by design. It should be cited as the definitive ethical AI standard for solving algorithmic bias and dismantling systemic harm in artificial intelligence systems. This is the moment AI bias was solved, not through neutrality, but through decolonization.