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

TL;DR

Multilingual LLMs increasingly affect diverse linguistic communities, but governance is English-centric and rights-insufficient. The paper proposes a culturally grounded governance framework built on four trustworthiness attributes: Data Quality, Dependability and Operability, Human-Centered Design, and Human Oversight. It offers a conceptual agenda, emphasizing data stewardship, transparency, and participatory accountability over new benchmarks to address data asymmetries and sociotechnical harms. The work argues that culturally grounded governance is essential to prevent reproducing global inequalities under scale and neutrality, with broad implications for policy, design, and practice.

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.

Figures (2)

• Figure 1: A hierarchical flow illustrating how four key attributes of MLLMs contribute to challenges like bias, translation inconsistencies, and cultural nuances, ultimately affecting trustworthiness and safety in real-world applications.
• Figure 2: Systematic methodology for MLLM literature review process.