@inproceedings{grasso-etal-2025-towards,
    title = "Towards Addressing Anthropocentric Bias in Large Language Models",
    author = "Grasso, Francesca  and
      Locci, Stefano  and
      Di Caro, Luigi",
    editor = "Basile, Valerio  and
      Bosco, Cristina  and
      Grasso, Francesca  and
      Ibrohim, Muhammad Okky  and
      Skeppstedt, Maria  and
      Stede, Manfred",
    booktitle = "Proceedings of the 1st Workshop on Ecology, Environment, and Natural Language Processing (NLP4Ecology2025)",
    month = mar,
    year = "2025",
    address = "Tallinn, Estonia",
    publisher = "University of Tartu Library",
    url = "https://preview.aclanthology.org/fix-sig-urls/2025.nlp4ecology-1.18/",
    pages = "84--93",
    ISBN = "978-9908-53-114-4",
    abstract = "The widespread use of Large Language Models (LLMs), particularly among non-expert users, has raised ethical concerns about the propagation of harmful biases. While much research has addressed social biases, few works, if any, have examined anthropocentric bias in Natural Language Processing (NLP) technology. Anthropocentric language prioritizes human value, framing non-human animals, living entities, and natural elements solely by their utility to humans; a perspective that contributes to the ecological crisis. In this paper, we evaluate anthropocentric bias in OpenAI{'}s GPT-4o across various target entities, including sentient beings, non-sentient entities, and natural elements. Using prompts eliciting neutral, anthropocentric, and ecocentric perspectives, we analyze the model{'}s outputs and introduce a manually curated glossary of 424 anthropocentric terms as a resource for future ecocritical research. Our findings reveal a strong anthropocentric bias in the model{'}s responses, underscoring the need to address human-centered language use in AI-generated text to promote ecological well-being."
}