Replication Data for: The erasure of intensive livestock farming in text-to-image generative AI
Bibliographic record
Abstract
Generative AI like ChatGPT has become increasingly integrated into people's daily lives as a vehicle for content creation and information access. While research has revealed how AI could perpetuate biases against marginalized human groups, AI’s impact on non-human animals remains understudied. We found that ChatGPT's text-to-image model (DALL-E 3) is biased toward romanticizing livestock farming as dairy cows grazing on pasture and pigs rooting in mud, even when explicitly prompted for realistic depictions. The public values naturalness and pasture access, but most farmed animals in industrialized countries are housed indoors and intensively. Notably, when we inhibited automatic prompt revision, images shifted to reflect modern farming more realistically: cows accessing feed through metal headlocks in intensive indoor systems, and pigs behind metal railings on concrete floors. While prompt revision was implemented to mitigate bias, it paradoxically erased intensive farming reality and conveyed disinformation that farmed animals live extensively.
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 | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.054 | 0.076 |
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".