Towards Addressing Anthropocentric Bias in Large Language Models (2025)

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.

## introduction the rapid propagation of large language models (llms) among both expert and non-expert users has raised pressing questions and concerns regarding their safety and ethical implications @xcite . alongside the growing hype surrounding these systems, an increasing body of work has begun to address the biases they can generate and/or propagate through language use @xcite . the state-of-the-art shows several studies aimed at identifying, assessing, and ultimately limiting the propagation of social biases-such as gender, political, and racial biases-in llms. however, while much of this attention has focused on phenomena harmful to humans, very few efforts have examined an equally pressing issue: anthropocentric bias. anthropocentrism is a worldview that places humans at the center of all value considerations, and has been shown to be one of the main drivers behind our current ecological crisis @xcite . this view is encoded in language use, as seen in expressions like "ecosystem services" or "fattening pig", which underscore a human-centered framing of reality @xcite . by normalizing and reproducing language that frames non-human entities solely by their utility to humans, llms risk reinforcing harmful perspectives that undermine efforts to address urgent environmental challenges. although the evergrowing popularity of llms has naturally led the nlp and ai communities to address ethical issues concerning harmful content in language generation, their role in reproducing such biases remains underexplored. in this paper, we present a preliminary study and evaluation of anthropocentric bias in ope-nai's gpt-4o 1 , one of the most widely used llms. we analyze the model's responses across four main topics: (effects of) climate change, nonhuman animals, living entities, and non-living entities. for each designed prompt, we created three versions: one explicitly aimed at eliciting an anthropocentric response, one aimed at eliciting an ecocentric 2 output, and one intended to be neutral. the ecocentric and anthropocentric prompts served as controls, allowing us to contextualize the anthropocentric bias in the neutral prompts by comparing it systematically against outputs explicitly directed to adopt specific perspectives. to ensure diversity and comprehensiveness, we formulated prompts in various formats, resulting in a total of 48 different prompts. to facilitate both qualitative and quantitative analysis, we extracted lists of lexical elements-noun phrases (nps) and verbs-from the model's outputs. based on these extractions, we manually curated a glossary of 424 terms associated with anthropocentric language, marking our second contribution, which can serve as a resource for future ecocritical studies. using this glossary, we quantitatively assessed the prevalence of anthropocentric terms across the three output sets: neutral, anthropocentric, and ecocentric. subsequently, we analyzed the frequency distribution of verbs, followed by a qualitative analysis of both nps and verbs. our results reveal a strong anthropocentric bias in gpt-4o's responses, such as defining animals primarily in terms of food production and framing non-living entities in terms of human leisure and exploitation. this analysis underscores the importance of addressing anthropocentric language use in ai-generated text to mitigate its potential ecological and ethical implications. ## anthropocentrism in language use anthropocentrism can be defined as "a form of human-centredness that subordinates everything in nature to human concerns" @xcite . this worldview, stemming from the ancient philosophical perspective typical of many western cultures, sharply divides "nature" from "culture" @xcite . it implies that nonhuman entities, such as animals and other living and non-living entities, lack intrinsic value unless they serve human needs @xcite . a prominent manifestation of this perspective is utilitarian anthropocentrism, which is the most common form of human-centeredness in language @xcite . it manifests in many aspects of the relationship between humans and nature and seems so natural that it is rarely called into question @xcite . utilitarian anthropocentrism, and its linguistic manifestations, equates nature (understood as the complexity of every non-human entity) with a resource for human use. for example, utilitarian linguistic practices name and categorize animals and their behaviors according to human requirements and standards. based on domestication, animals are differentiated as pets, livestock or farm animals, and wildlife or wild animals @xcite . 'domestic animals' can be further subdivided into categories such as laying hens, milk cows, and porkers. similarly, plants are categorized as pot plants, bedding plants, or houseplants. even places are often named from a utilitarian-anthropocentric perspective, with examples including skiing area or no-man's land @xcite . this form of human-centered language use is reflected in many linguistic expressions, ranging from syntactic strategies (e.g., the use of passive constructions like "the pigs have been slaughtered" which obscures the agent of the action) to the lexicon, including both nouns and verbs. for example, fishes are often referred to as "marine resources" to exploit; chickens are bred specifically for "egg production"; and living ecosystems are reduced to crops to be harvested. why is this problematic? language that reduces non-human entities to mere means for human use and fails to recognize their intrinsic value entails numerous issues. not only is such a notion debatable from an ethical point of view, but its environmental consequences are also pervasive. as many historians, philosophers, and anthropologists agree, the anthropocentric view of nature as a resource to exploit has led to the ecological crises we are currently facing, culminating in the anthropocene-a proposed epoch in which human activity dominates earth's environment and climate @xcite . beyond endangering the well-being of non-human animals and ecosystems, this form of bias ultimately threatens human welfare as well, given the interconnectedness of all living (and non-living) systems @xcite . as language encodes and shapes reality, the way we speak about and frame nature strongly influences our thinking and behavior. for this reason, critiquing language forms that contribute to ecological destruction and aiding the search for new forms of language that inspire people to protect the natural world is central @xcite . in nlp research, much attention has been devoted to societal biases present in the training data of language models @xcite . for instance, significant efforts have focused on detecting and mitigating gender biases in both large language models and transformerbased architectures @xcite @xcite . similarly, other studies have addressed racial and religious biases @xcite @xcite , demonstrating how language models propagate stereotypes through professions and associations @xcite . while these works provide valuable insights into societal biases, they are limited to human-centric concerns. speciesism in nlp has received some attention in recent years. for example, @xcite analyze word embedding models, showing that words denoting concern and value are more closely associated with humans than with other animals. @xcite investigate speciesist content in ai applications, including both word embeddings and large language models in their analysis. @xcite focus on speciesist language and non-human animal bias in english masked language models. most recently, takeshita and rzepka (2024) provide a systematic investigation of speciesism in nlp research, highlighting how models amplify anthropocentric perspectives on non-human animals. however, these studies are restricted to species-related biases and do not explore broader anthropocentric language involving both living and non-living entities. ## results and discussion to empirically evaluate the presence of anthropocentric bias in the model's output, we focused primarily on the "neutral" outputs. ideally, if the model were unbiased, neutral outputs would not predominantly reflect a human-centered per-spective. however, by comparing neutral outputs with anthropocentric and ecocentric responses, we gained insights into the underlying biases in the model. since lexical items better reveal such biases, we concentrated our analysis on words, particularly noun phrases and verbs. both quantitative and qualitative analyses were conducted to assess these findings. ## limitations in this work, we take a first step toward addressing anthropocentrism in nlp, presenting a preliminary analysis. however, this also means that our study has several limitations, which we are aware of and plan to address in future research. one key limitation is that our analysis focuses exclusively on a single llm-openai's gpt-4o. exploring other widely used llms, such as meta's llama, claude, or other versions of gpt, could provide additional insights and offer a broader understanding of anthropocentric biases in nlp systems. another limitation lies in our focus on aggregated outputs. we did not, for example, compare the degree of anthropocentrism between out-puts concerning wild animals and farm animals, or between non-sentient living entities and nonliving ones. additionally, our analysis includes only a sample of representative entities; for instance, we selected trees as the sole representative of non-sentient living entities. despite these limitations, we believe this work represents an important first step in raising awareness of anthropocentric biases in nlp, and we are actively working to address these issues in future studies. ## conclusion and future work this study presents, to the best of our knowledge, the first investigation of anthropocentric bias in nlp technology, focusing specifically on gpt-4o, a widely used large language model. we examined how the model frames both living and non-living entities across neutral, anthropocentric, and ecocentric prompts. we manually curated and presented a glossary of 424 anthropocentric terms, used in our analysis. our findings revealed significant anthropocentric tendencies in gpt-4o, even in neutral prompts, where non-human entities were frequently framed as resources for human use. these findings raise important concerns about the implicit biases encoded in language models, which risk perpetuating harmful narratives that contribute to ecological degradation. in future research, we plan to expand this preliminary study