The erasure of intensive livestock farming in text-to-image generative AI
This research highlights a bias in AI that misrepresents intensive livestock farming, potentially misleading the public about animal welfare and environmental impacts, though it is incremental as it builds on known AI bias issues.
The study found that ChatGPT's DALL-E 3 text-to-image model introduces a strong bias toward romanticizing livestock farming, such as depicting dairy cows on pasture, which contrasts with the reality of indoor farming practices in industrialized countries; inhibiting automatic prompt revision mitigated this bias, producing images more reflective of modern farming, like cows indoors with metal headlocks.
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.