# Choosing a Model, Shaping a Future: Comparing LLM Perspectives on Sustainability and its Relationship with AI

arXiv: https://arxiv.org/abs/2505.14435 · HTML: https://mlsanity.com/paper/2505.14435/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 26 references that are in the map.
- First posted on arXiv 20 May 2025.

## Builds on (most cited first, 4 of 4)

- ["Kelly is a Warm Person, Joseph is a Role Model": Gender Biases in LLM-Generated Reference Letters](https://mlsanity.com/paper/2310.09219/) (2023)
- [Whose Morality Do They Speak? Unraveling Cultural Bias in Multilingual Language Models](https://mlsanity.com/paper/2412.18863/) (2024)
- [A Cautionary Tale About "Neutrally" Informative AI Tools Ahead of the 2025 Federal Elections in Germany](https://mlsanity.com/paper/2502.15568/) (2025)
- [Is GPT-4 Less Politically Biased than GPT-3.5? A Renewed Investigation of ChatGPT's Political Biases](https://mlsanity.com/paper/2410.21008/) (2024)

## Built on by (most cited first, 2 of 2)

- [A Detailed Study on LLM Biases Concerning Corporate Social Responsibility and Green Supply Chains](https://mlsanity.com/paper/2511.01840/) (2025)
- [Greener Than Humans? Environmental Attitudes in Large Language Models](https://mlsanity.com/paper/2606.02741/) (2026)

## Abstract (arXiv)

As organizations increasingly rely on AI systems for decision support in sustainability contexts, it becomes critical to understand the inherent biases and perspectives embedded in Large Language Models (LLMs). This study systematically investigates how five state-of-the-art LLMs -- Claude, DeepSeek, GPT, LLaMA, and Mistral - conceptualize sustainability and its relationship with AI. We administered validated, psychometric sustainability-related questionnaires - each 100 times per model -- to capture response patterns and variability. Our findings revealed significant inter-model differences: For example, GPT exhibited skepticism about the compatibility of AI and sustainability, whereas LLaMA demonstrated extreme techno-optimism with perfect scores for several Sustainable Development Goals (SDGs). Models also diverged in attributing institutional responsibility for AI and sustainability integration, a results that holds implications for technology governance approaches. Our results demonstrate that model selection could substantially influence organizational sustainability strategies, highlighting the need for awareness of model-specific biases when deploying LLMs for sustainability-related decision-making.