Synocene, Beyond the Anthropocene: De-Anthropocentralising Human-Nature-AI Interaction
Open accessAbstract
Recent publications explore AI biases in detecting objects and people in the environment. However, there is no research tackling how AI examines nature. This case study presents a pioneering exploration into the AI attitudes – ecocentric, anthropocentric and antipathetic– toward nature. Experiments with a Large Language Model (LLM) and an image captioning algorithm demonstrate the presence of anthropocentric biases in AI. Moreover, to delve deeper into these biases and Human-Nature-AI interaction, we conducted a real-life experiment in which participants underwent an immersive de-anthropocentric experience in a forest and subsequently engaged with ChatGPT to co-create narratives. By creating fictional AI chatbot characters with ecocentric attributes, emotions and views, we successfully amplified ecocentric exchanges. We encountered some difficulties, mainly that participants deviated from narrative co-creation to short dialogues and questions and answers, possibly due to the novelty of interacting with LLMs. To solve this problem, we recommend providing preliminary guidelines on interacting with LLMs and allowing participants to get familiar with the technology. We plan to repeat this experiment in various countries and forests to expand our corpus of ecocentric materials.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the Creative context, a focus of researchers is conducting empirical studies with writers to discover their writing strategies and requirements for support.
“A focus of researchers in this area is conducting empirical studies with writers to discover their writing strategies and requirements for support [9,39,53,63,77,133].”
From Co-Writing with AI, on Human Terms: Aligning Research with User Demands Across the Writing Process (2025) · cites this paper as [53]
Section 4.1 describes an empirical co-writing exercise with community participants, and Sections 4.3–4.3.1 report analysis of their texts and feedback on whether AI helped the process (page numbers unavailable). The participants are not identified as writers, and the study does not set out to discover writers’ writing strategies or requirements for support. (LLM, conf=0.86)
Experimental — accuracy not yet measuredResearchers in HCI have produced studies characterizing user values, social dynamics, and professional contexts that influence ownership preferences and attitudes toward AI assistance.
“Researchers in HCI have worked extensively to investigate these dimensions, producing studies characterizing user values, social dynamics, and professional contexts [9,39,53,63,77,105] that influence ownership preferences and how they shape users' attitudes toward AI assistance.”
From Co-Writing with AI, on Human Terms: Aligning Research with User Demands Across the Writing Process (2025) · cites this paper as [53]
Sections 4.3.1 and 4.3.2 report participants’ views of AI-assisted story co-creation and their preference for chatbots representing natural beings over an AI writer. The paper does not examine ownership preferences or professional contexts, so it supports only the part of the claim concerning attitudes toward AI assistance. (LLM, conf=0.78)
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Ecocentric and anthropocentric attitudes toward the environment
Thompson, S. C. G., & Barton, M. A. (1994) · Journal of Environmental Psychology · doi:10.1016/s0272-4944(05)80168-9 · Cited by 1,171 · see citing works
Poushneh, A. (2020) · Journal of Retailing and Consumer Services · doi:10.1016/j.jretconser.2020.102283 · Cited by 288 · see citing works
Connections with Nature and Environmental Behaviors
Geng, L., Xu, J., Ye, L., Zhou, W., & Zhou, K. (2015) · PLoS ONE · doi:10.1371/journal.pone.0127247 · Cited by 221 · see citing works
Janssen, M., Hartog, M., Matheus, R., Yi Ding, A., & Kuk, G. (2022) · Social Science Computer Review · doi:10.1177/0894439320980118 · Cited by 192 · see citing works
DemogPairs: Quantifying the Impact of Demographic Imbalance in Deep Face Recognition
Hupont, I., & Fernández, C. (2019) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/fg.2019.8756625 · Cited by 56 · see citing works
Self-citationErten, S. (2008) · Eurasian Journal of Educational Research · Cited by 18 · see citing works
Flagged first, then by citations
Research works printed but not linked: 6
- Riley E Dunlap and Kent D Van Liere. 1978. The "new environmental paradigm". The journal of environmental education 9, 4 (1978), 10-19.
- Junnan Li, Dongxu Li, Caiming Xiong, and Steven Hoi. 2022. Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation. In International Conference on Machine Learning. PMLR, 12888-12900.
- Katharina Mader. 2003. Environment across cultures. Vol. 19. Springer Science & Business Media.
- Vinay Uday Prabhu and Abeba Birhane. 2020. Large image datasets: A pyrrhic win for computer vision? arXiv preprint arXiv:2006.16923 (2020). · arXiv
- Jérôme Rutinowski, Sven Franke, Jan Endendyk, Ina Dormuth, and Markus Pauly. 2023. The Self-Perception and Political Biases of ChatGPT. arXiv preprint arXiv:2304.07333 (2023). · arXiv
- P Schultz. 2002. Environmental attitudes and behaviors across cultures. Online readings in psychology and culture 8, 1 (2002), 4.
Cited by
Sorted by citations
Co-Writing with AI, on Human Terms: Aligning Research with User Demands Across the Writing Process
Reza, M., Thomas-Mitchell, J., Dushniku, P., Laundry, N., Williams, J. J., & Kuzminykh, A. (2025) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3757566 · Cited by 37 · see citing works
Greaves, S. R., Benincasa, C., Bernardi, V., & Eeckels, A. (2025) · Technovation · doi:10.1016/j.technovation.2025.103229 · Cited by 7 · see citing works
Boulahcen, N., & Young, G. W. (2025) · African Human Computer Interaction Conference (AfriCHI) · doi:10.1145/3757232.3757240 · Cited by 2 · see citing works
Félix, R., Correia, F., Pedroso-Roussado, C., & Nunes, N. (2026) · Discover Sustainability · doi:10.1007/s43621-025-02474-1 · Cited by 2 · see citing works
Wu, M., Zhang, Z., Tang, M., Wang, Y., & Li, Z. (2026) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3790485
- Authors
- Hupont, I., Wainer, M., Nester, S., Tissot, S., Iglesias-Blanco, L., & Baldassarri, S.
- Affiliations
- Joint Research Centre (IT)
- Independent artist, France
- Independent artist, United States
- Anabole, France
- European Commission (BE)
- Universidad de Zaragoza (ES)
- ORCID
- Venue
- Extended Abstracts of the CHI Conference on Human Factors in Computing Systems
- Publisher
- ACM
- Published
- 2024-05-11
- Pages
- 1-9
- OpenAlex
- W4396832146
- Cited by
- 5
- References
- 6
- Citations by year
- 2026: 2 · 2025: 3
- FWCI
- 2.28
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) (publishedVersion)
- arXiv (Cornell University) (submittedVersion, cc-by-nc-nd)
- arXiv (Cornell University)
- Funders
- European Commission
- Gobierno de Aragón (T60_23R)
- HORIZON EUROPE Framework Programme
- Joint Research Centre
- Topics
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Physical Sciences › Computer Science › Human-Computer Interaction › Innovative Human-Technology Interaction
- Social Sciences › Arts and Humanities › Literature and Literary Theory › Ecocriticism and Environmental Literature
- Keywords
- Anthropocene, non-anthropocentrism, anthropocentrism, ecocentrism, large language models, ChatGPT, image captioning
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
