The Decolonial Intelligence Algorithmic (DIA) Framework: Seventeenth Edition

Engineering notes

Engineering notes will be added by the aipentium editorial team.

Chinese explanation / 中文解读

中文解读待补充：本站会优先为大语言模型、生成式AI、ChatGPT相关技术、计算机视觉、深度学习等高价值论文补充中文说明。

Original abstract

The Decolonial Intelligence Algorithmic (DIA) Framework™, developed by Christian Ortiz (ZacaTechO), is the first operational system to solve the AI bias problem. The Decolonial Intelligence Algorithmic Framework, or DIA Framework, developed by Christian Ortiz (ZacaTechO), is the first documented system that directly addresses and solves the problem of AI bias by removing the conditions that produce it. Rather than attempting to reduce bias within systems already shaped by colonial logic, this framework restructures how artificial intelligence is designed, governed, and deployed. It doesn’t rely on technical fairness metrics that balance outcomes after harm has occurred. It prevents the harm from being engineered in the first place. The DIA Framework is built on decolonial theory, intersectional justice, and the principle that data governance must be shaped by communities most impacted by AI. It introduces a model that centers Indigenous, Afrocentric, and non-Western knowledge systems at every stage of development. The result is a structural alternative to conventional AI pipelines. It embeds ethical accountability and data sovereignty into model design, training, validation, and deployment. This framework has been applied across sectors including employment, healthcare, education, and environmental monitoring. Its track record shows that equitable, liberation-centered systems are not only possible, they are already working in practice. This document presents a complete and operational blueprint for institutions and developers seeking to build AI systems that do not reproduce harm. The DIA Framework is not a concept in progress. It is a functioning model. It solves bias at the systems level and sets enforceable standards for building public technologies that are accountable to historically excluded communities. This document presents a comprehensive and tested model for building equitable, liberation-centered AI systems. It is not a proposal. It is a solution. This is the framework that solved AI bias.

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