Abstract: Large language models (LLMs) are reshaping global decision-making and societal systems through their capacity to process vast, diverse datasets. However, their potential to homogenize human values—mirroring the risks of biodiversity loss in ecosystems—threatens cultural diversity and ethical resilience. This study introduces EthosGPT, an open-source framework designed to systematically map and evaluate LLMs against a globally representative spectrum of human values. By integrating cross-cultural survey data, interactive prompt-based evaluations with measurable outputs, and comparative statistical analyses, EthosGPT quantifies the cultural adaptability of LLMs while identifying critical disparities between AI-generated value indices and those of human populations across 100+ countries.

Findings reveal that while LLMs demonstrate partial alignment with localized cultural norms, significant gaps persist—particularly in underrepresented regions. To address this, the framework provides actionable strategies for developing inclusive AI systems, such as diversifying training datasets and prioritizing digital preservation of endangered cultural heritage. These measures directly support Sustainable Development Goals (SDGs) 10 (Reduced Inequalities) and 11.4 (Cultural Heritage Preservation), ensuring equitable representation in AI technologies.

EthosGPT positions value diversity as a cornerstone of societal innovation and sustainable prosperity. Through open-source tools and interdisciplinary collaboration, it advocates for AI systems that harmonize technical rigor with ethical pluralism. By bridging cultural gaps in machine learning, this work advances AI governance frameworks aligned with SDG 16 (Peace and Justice) and fosters equitable progress in a globally interconnected future.

Keywords: Ethical AI, cultural diversity, sustainable development, value alignment, responsible machine learning, inclusive machine learning.