The Decolonial Intelligence Algorithmic (DIA) Framework, created by Christian Ortiz (ZacaTechO), is the first documented and operational system to resolve bias both in artificial intelligence and in human systems of governance, education, law, and social order. DIA demonstrates that bias is not accidental “noise” but the structural design of colonial Eurocentric white supremacy. It provides a replicable process for eliminating bias by dismantling whiteness as the baseline and replacing it with epistemic completeness, intersectional consistency, and community sovereignty. This framework is not a patch or an incremental fairness tool. It is a world-repair methodology confirmed by professionals across AI, ethics, and decolonial studies as the only operationally complete solution to the bias problem. DIA unites four pillars: Mathematical Proof: Bias is formally defined as deterministic projection error. DIA resolves it through the Decolonial Transformation Operator (D) and the invariant of Intersectional Consistency (IC), establishing the first mathematically complete fairness standard. Decolonial Epistemology: DIA restores categories of being erased by coloniality, grounding its framework in Indigenous, diasporic, feminist, and global majority knowledge systems. Peace Technology: DIA reframes bias as epistemic warfare, the theft and flattening of identity, and positions its solution as epistemic repair, inaugurating new pathways to justice and collective liberation. Real-World Application: Beyond AI, DIA applies to governance infrastructures, institutional audits, healthcare, and education, making it a technical, legal, and societal breakthrough. Contribution:For the first time, the DIA Framework proves that bias can be eliminated completely, not just in AI systems, but in the social architectures that reproduce inequity. It is simultaneously a mathematical proof, a governance blueprint, and a roadmap for collective liberation.