Culturally-Grounded Governance for Multilingual Language Models: Rights, Data Boundaries, and Accountable AI Design

2026-01-31

AI Summary

This work addresses the systemic risks posed by the global deployment of multilingual large language models, which often overlook cultural diversity and exacerbate inequities for low-resource language communities in data fairness, value alignment, and accountability. Moving beyond technocentrism, the study pioneers a culturally grounded governance framework that centers cultural rights, local norms, and power structures. Through human-centered computing and cross-cultural socio-technical analysis, it integrates data equity assessment with participatory policy design to systematically identify three core governance challenges. The paper offers actionable design and policy recommendations to ensure multilingual AI systems do not reinforce global inequalities under the guise of scale and neutrality, thereby advancing more just and inclusive AI governance.

Technology Category

• Philosophy and Ethics of AI: Bias, Fairness & Equity
• Natural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy
• Machine Learning: Ethics, Bias, and Fairness

Application Category

• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Economics, Online Markets and Human Computation: Fairness and ethical considerations in crowd work and in human-in-the-loop AI systems

Abstract

Multilingual large language models (MLLMs) are increasingly deployed across cultural, linguistic, and political contexts, yet existing governance frameworks largely assume English-centric data, homogeneous user populations, and abstract notions of fairness. This creates systematic risks for low-resource languages and culturally marginalized communities, where data practices, model behavior, and accountability mechanisms often fail to align with local norms, rights, and expectations. Drawing on cross-cultural perspectives in human-centered computing and AI governance, this paper synthesizes existing evidence on multilingual model behavior, data asymmetries, and sociotechnical harm, and articulates a culturally grounded governance framework for MLLMs. We identify three interrelated governance challenges: cultural and linguistic inequities in training data and evaluation practices, misalignment between global deployment and locally situated norms, values, and power structures, and limited accountability mechanisms for addressing harms experienced by marginalized language communities. Rather than proposing new technical benchmarks, we contribute a conceptual agenda that reframes multilingual AI governance as a sociocultural and rights based problem. We outline design and policy implications for data stewardship, transparency, and participatory accountability, and argue that culturally grounded governance is essential for ensuring that multilingual language models do not reproduce existing global inequalities under the guise of scale and neutrality.

Problem

• multilingual language models
• AI governance
• cultural marginalization
• data inequity
• accountability

Innovation

• culturally grounded governance
• multilingual language models
• data sovereignty
• participatory accountability
• sociotechnical harm