Meet Christian Ortiz, a.k.a. ZacaTechO — The Afro-Indigenous Visionary Who Solved the AI Bias Problem
Bibliographic record
Abstract
This article profiles Christian Ortiz, also known as ZacaTechO, the Afro-Indigenous technologist who designed and programmed the world’s first decolonial artificial intelligence system to solve the root causes of algorithmic bias. Ortiz's creation, Justice AI GPT, powered by his Decolonial Intelligence Algorithmic Framework (DIA), moves beyond traditional AI ethics to abolish the structural logic of white supremacy embedded in machine learning systems. Built independently and programmed entirely by Ortiz, Justice AI is supported by over 16,800 years of recorded knowledge contributed by 560+ global Decolonial scholars and knowledge keepers, making it the largest liberation-centered dataset in history. The system is LLM-agnostic, multilingual, trauma-informed, and fully aligned with global civil rights and algorithmic governance frameworks like the EEOC, ADA, and EU AI Act. Rooted in Indigenous and Afro-diasporic epistemologies, it prioritizes interdependence over optimization and centers lived experiences of historically oppressed communities. A landmark moment occurred when Ortiz presented Justice AI live at the 2025 Mesh Conference in Calgary, delivering a real-time bias audit that visibly shifted the energy of the room. Ortiz also credits a global lineage of resistance and brilliance, from Joy Buolamwini and Safiya Umoja Noble to Rediet Abebe and grassroots collectives like Black in AI, as foundational to his work. Justice AI, as this article argues, isn’t simply an innovation. It’s a paradigm shift. And Christian Ortiz may very well become the name synonymous with the moment AI moved from empire toward collective liberation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".