Black Digital Models and the Ethics of Representation: An Autotheoretical Intervention
Bibliographic record
Abstract
When AI speaks in Blackness, whose voice is heard, and whose is silenced? This paper examines Shudu, a computer-generated Black model with over 237,000 Instagram followers, created by a White male artist, through the lens of autotheory. As both method and critique, autotheory allows embodied narrative to enter into dialogue with cultural theory, creating an intervention rarely seen in communication scholarship. This approach makes visible how digital simulations of Blackness operate as communicative acts: they reproduce racial capitalism, commodify Black esthetics, erase Black agency, and perform false inclusion. By situating Shudu as a digital human rights dilemma, the research critiques the use of AI-generated models in the fashion industry, highlighting ethical concerns related to racial plagiarism, cultural appropriation, and labor displacement. By replacing real Black models with CGI alternatives, corporations avoid responsibilities tied to fair wages, authentic representation, and systemic equity. Moreover, Shudu mirrors historical patterns of exploitation, reinforcing a cyber plantation system where Blackness is monetized without benefiting Black individuals. In foregrounding autotheory, this study not only critiques the exploitation of Black representation in digital environments but also demonstrates the promise of autotheory as a methodological expansion for cultural communication research.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".