(untitled) (International Journal of Artificial Intelligence Engineering and Systems, 2026)
As foundation models increasingly mediate visual culture across global audiences, their capacity to fairly represent diverse cultural contexts has emerged as a pressing socio-technical challenge. Current text-to-image generation systems exhibit profound demographic and cultural biases, often defaulting to Western, high-resource visual norms while erasing or stereotyping underrepresented communities. This paper presents a system-level analysis of fairness-aware benchmarking for cross-cultural visual generation, arguing that effective evaluation requires far more than dataset curation or prompt engineering. We articulate how benchmark design must integrate infrastructure scalability, metric pluralism, governance scaffolding, and sustainability considerations into a coherent sociotechnical architecture. Through a critical examination of existing evaluation pipelines, we identify structural trade-offs between cultural granularity and computational tractability, between automated metrics and culturally grounded human evaluation, and between centralised model auditing and community-driven oversight. Drawing on insights from recent model deployments, we propose a layered benchmarking architecture that couples large-scale automated analysis with distributed cultural annotation networks, enabling continuous monitoring of representation gaps. We further discuss the role of regulatory frameworks, model cards, and participatory design in shaping accountability for cross-cultural fairness. Throughout, we emphasise that cross-cultural fairness in generative AI is not a static property to be optimised but a dynamic, contested terrain that demands ongoing institutional commitment, reflexive evaluation practices, and infrastructure that supports pluralism rather than erasing it. The paper contributes a comprehensive framework for thinking about fairness-aware benchmarking as a system of measurement, governance, and cultural negotiation.