A Risk-Aware Governance Framework for Cultural Diversity Preservation in Next-Generation Text-to-Image Foundation Models (International Journal of Artificial Intelligence Engineering and Systems, 2026)
The rapid proliferation of text-to-image foundation models has introduced transformative capabilities for creative production and knowledge communication, yet it has simultaneously surfaced deep structural risks associated with the systematic erosion of cultural diversity. This paper proposes a risk-aware governance framework that integrates cultural preservation safeguards directly into the lifecycle of next-generation text-to-image systems, from data curation and training pipelines to deployment infrastructure and post-deployment monitoring. The framework is grounded in a systems-level analysis that treats cultural diversity not as an isolated fairness metric but as a dynamic, multiscalar property shaped by interactions among training data provenance, model architecture, prompt ingestion mechanisms, inference acceleration stacks, and the socio-technical contexts of use. The discussion articulates critical structural trade-offs, including those between representational completeness and computational efficiency, between centralized content moderation and decentralized cultural sovereignty, and between open-ended generative capability and the precautionary containment of cultural harms. Governance mechanisms such as stratified risk registers, culture-aware red-teaming, data documentation protocols, and continuous auditing loops are examined as components of an infrastructural intervention. The paper further explores the sustainability and robustness dimensions of cultural diversity preservation under continual learning and model updating scenarios, and it assesses cross-domain policy implications for AI regulation, digital heritage protection, and international technology governance. By foregrounding architectural, operational, and policy-level levers, the framework advances a holistic approach to ensuring that next-generation foundation models become sites of cultural pluralism rather than agents of cultural flattening.
Keywords: Text-to-image generation, foundation models, cultural diversity, risk governance, fairness, AI infrastructure, sustainability