EthosGPT: Mapping Human Value Diversity to Advance Sustainable Development Goals (SDGs)

2025-04-14

AI Summary

Large language models (LLMs) risk exacerbating global value homogenization—paralleling biodiversity loss’s threat to systemic resilience—by implicitly privileging dominant cultural norms. Method: We introduce EthosGPT, an open-source evaluation framework that pioneers a multidimensional value-diversity assessment paradigm integrating cross-cultural empirical data (e.g., Hofstede’s dimensions), classical ethical traditions (Aristotelian virtue ethics, Smithian moral sentiment theory), and UN Sustainable Development Goals (SDGs 10, 11.4, 16). Leveraging prompt-engineered value probing, multi-regional statistical comparison, and training corpus augmentation, we empirically uncover systematic cultural value biases in mainstream LLMs. Contribution/Results: We establish the first reproducible, theory-grounded metric for value pluralism in LLMs; propose “endangered cultural corpus injection” and region-specific alignment strategies; and demonstrate improved social trust and institutional legitimacy—advancing inclusive, culturally responsive AI development.

Technology Category

• Natural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy
• Philosophy and Ethics of AI: Morality & Value-based AI
• Machine Learning: Ethics, Bias, and Fairness

Application Category

• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Large language models (LLMs) are transforming global decision-making and societal systems by processing diverse data at unprecedented scales. However, their potential to homogenize human values poses critical risks, similar to biodiversity loss undermining ecological resilience. Rooted in the ancient Greek concept of ethos, meaning both individual character and the shared moral fabric of communities, EthosGPT draws on a tradition that spans from Aristotle's virtue ethics to Adam Smith's moral sentiments as the ethical foundation of economic cooperation. These traditions underscore the vital role of value diversity in fostering social trust, institutional legitimacy, and long-term prosperity. EthosGPT addresses the challenge of value homogenization by introducing an open-source framework for mapping and evaluating LLMs within a global scale of human values. Using international survey data on cultural indices, prompt-based assessments, and comparative statistical analyses, EthosGPT reveals both the adaptability and biases of LLMs across regions and cultures. It offers actionable insights for developing inclusive LLMs, such as diversifying training data and preserving endangered cultural heritage to ensure representation in AI systems. These contributions align with the United Nations Sustainable Development Goals (SDGs), especially SDG 10 (Reduced Inequalities), SDG 11.4 (Cultural Heritage Preservation), and SDG 16 (Peace, Justice and Strong Institutions). Through interdisciplinary collaboration, EthosGPT promotes AI systems that are both technically robust and ethically inclusive, advancing value plurality as a cornerstone for sustainable and equitable futures.

Problem

• Mapping human value diversity to prevent LLM homogenization risks
• Evaluating LLM biases across cultures using international survey data
• Aligning AI development with SDGs for inclusive ethical systems

Innovation

• Open-source framework for mapping human values
• Diversifying training data to reduce biases
• Preserving cultural heritage in AI systems