mlsanityData to 14 Oct 2026

The erasure of intensive livestock farming in text-to-image generative AI

arXiv 2502.19771 ↗First posted 27 Feb 2025cs.CY cs.AIcited by 1

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Abstract (arXiv)

Generative AI (e.g., ChatGPT) is increasingly integrated into people's daily lives. While it is known that AI perpetuates biases against marginalized human groups, their impact on non-human animals remains understudied. We found that ChatGPT's text-to-image model (DALL-E 3) introduces a strong bias toward romanticizing livestock farming as dairy cows on pasture and pigs rooting in mud. This bias remained when we requested realistic depictions and was only mitigated when the automatic prompt revision was inhibited. Most farmed animal in industrialized countries are reared indoors with limited space per animal, which fail to resonate with societal values. Inhibiting prompt revision resulted in images that more closely reflected modern farming practices; for example, cows housed indoors accessing feed through metal headlocks, and pigs behind metal railings on concrete floors in indoor facilities. While OpenAI introduced prompt revision to mitigate bias, in the case of farmed animal production systems, it paradoxically introduces a strong bias towards unrealistic farming practices.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 3 of its 65 references that are in the map. First posted on arXiv 27 Feb 2025.

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3 of 65 references.

References · 3

  1. 2024Survey of Bias In Text-to-Image Generation: Definition, Evaluation, and Mitigationcited by 66
  2. 2024Fairness Feedback Loops: Training on Synthetic Data Amplifies Biascited by 55
  3. 2023AI's Regimes of Representation: A Community-centered Study of Text-to-Image Models in South Asiacited by 54

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