A Detailed Study on LLM Biases Concerning Corporate Social Responsibility and Green Supply Chains
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866917057026588672 |
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| author | Ontrup, Greta Bush, Annika Pauly, Markus Aksoy, Meltem |
| author_facet | Ontrup, Greta Bush, Annika Pauly, Markus Aksoy, Meltem |
| contents | Organizations increasingly use Large Language Models (LLMs) to improve supply chain processes and reduce environmental impacts. However, LLMs have been shown to reproduce biases regarding the prioritization of sustainable business strategies. Thus, it is important to identify underlying training data biases that LLMs pertain regarding the importance and role of sustainable business and supply chain practices. This study investigates how different LLMs respond to validated surveys about the role of ethics and responsibility for businesses, and the importance of sustainable practices and relations with suppliers and customers. Using standardized questionnaires, we systematically analyze responses generated by state-of-the-art LLMs to identify variations. We further evaluate whether differences are augmented by four organizational culture types, thereby evaluating the practical relevance of identified biases. The findings reveal significant systematic differences between models and demonstrate that organizational culture prompts substantially modify LLM responses. The study holds important implications for LLM-assisted decision-making in sustainability contexts. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_01840 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Detailed Study on LLM Biases Concerning Corporate Social Responsibility and Green Supply Chains Ontrup, Greta Bush, Annika Pauly, Markus Aksoy, Meltem Computers and Society Artificial Intelligence Organizations increasingly use Large Language Models (LLMs) to improve supply chain processes and reduce environmental impacts. However, LLMs have been shown to reproduce biases regarding the prioritization of sustainable business strategies. Thus, it is important to identify underlying training data biases that LLMs pertain regarding the importance and role of sustainable business and supply chain practices. This study investigates how different LLMs respond to validated surveys about the role of ethics and responsibility for businesses, and the importance of sustainable practices and relations with suppliers and customers. Using standardized questionnaires, we systematically analyze responses generated by state-of-the-art LLMs to identify variations. We further evaluate whether differences are augmented by four organizational culture types, thereby evaluating the practical relevance of identified biases. The findings reveal significant systematic differences between models and demonstrate that organizational culture prompts substantially modify LLM responses. The study holds important implications for LLM-assisted decision-making in sustainability contexts. |
| title | A Detailed Study on LLM Biases Concerning Corporate Social Responsibility and Green Supply Chains |
| topic | Computers and Society Artificial Intelligence |
| url | https://arxiv.org/abs/2511.01840 |