Decolonial Prompting: Rewriting AI Toward Black Futures
Bibliographic record
Abstract
This presentation develops a theory of decolonial prompting as a method of engaging large language models (LLMs) and other AI systems from the perspective of Black studies and decolonial thought. It argues that prompting is not a neutral technical skill but a political act situated within the coloniality of power, where race, knowledge, and humanity have been historically organized through Eurocentric hierarchies. Drawing on Quijano, Mignolo, and Wynter, the talk traces how modern AI systems inherit and reproduce colonial logics in their training data, defaults, and interfaces. It then introduces decolonial prompting as a practical method for Black scholars and communities to contest erasure, expose algorithmic anti-Blackness, and rewrite machinic outputs toward Black futures. Using case studies from my experience with OpenAI’s Sora and Canva, alongside mainstream chat and image models, the presentation shows how visual and textual outputs often encode Blackness as deficit or pathology. It maps the layered subject positions involved in prompting such as colonized subjects, programmers, validators, and corporate/state actors—and argues that every prompt is a negotiation between subject, object, and frame. The slide deck was prepared for the African Studies conference panel on AI, race, and decoloniality with the full paper published by the Cambridge University Press in the African Studies Review in 2026. It is intended both as a conceptual intervention and a practical resource for those seeking to engage AI critically and strategically, and in solidarity with Black and other marginalized communities.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.008 | 0.001 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.007 |
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; both teacher heads agree on what is shown here.
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".