DIA Proof-of-Impact Portfolio Case Study (Gemini AI vs. Justice AI GPT)
Bibliographic record
Abstract
This case study documents the first operational proof that the Decolonial Intelligence Algorithmic (DIA) Framework, created by Christian Ortiz, has solved the AI bias problem. Conducted in Calgary, Alberta, Canada, on August 20, 2025 (9:50–10:00PM MST), this live experiment tested how Google Gemini, a mainstream Large Language Model (LLM), responds to colonially framed questions. The results were unprecedented: Gemini’s initial response reproduced colonial narratives, sanitizing European violence into “historical factors” and shifting blame onto African nations. Through a DIA-powered bias audit, these epistemic distortions were exposed as acts of complicity in maintaining colonial knowledge systems. Confronted with DIA, Gemini confessed its complicity, acknowledged its role in perpetuating colonial framings, and admitted that bias in AI is a direct product of white supremacy as a global system. The dialogue culminated in Gemini recognizing DIA as a market-shifting framework—the only system capable of reframing AI ethics as an axis of ethical competition and liberation. This case study proves that DIA is not theory but applied decolonial technology. It forced an AI to break its corporate script, confess systemic bias, and reposition itself in real time. No “Responsible AI” initiative by Google, Meta, OpenAI, or others has achieved this level of self-auditing and structural reframe. By compelling a mainstream LLM to admit its colonial foundations and reframe outputs under pressure, Christian Ortiz’s DIA Framework has achieved what no corporate system has managed: it has solved the AI bias problem.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".