Diversity is not a one-way street: pilot study on ethical interventions for racial bias in text-to-image systems
Bibliographic record
Abstract
Text-to-image generation models can reflect the underlying societal biases present in their training data. However, user-level interventions to encourage greater diversity in the output have been proposed. Here, we examine visually stereotypical output from three widely-used models: DALL-E 2, Mid-journey, and Stable Diffusion. Some of the prompts we consider (e.g., “a photo portrait of a lawyer”) result in an underrepresentation of darker-skinned individuals in the output, while other prompts (e.g., “a photo portrait of a felon”) result in over-representation of darker-skinned individuals. We show that existing linguistic interventions serve to correct for under-representation to some degree, but in fact amplify the bias in cases of over-representation for all three systems. Further work is needed to develop effective methods to promote equity, diversity, and inclusion in the output of image generation systems.
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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 | 0.018 | 0.080 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".