Cultural Bias Auditing in Multimodal Generative Models Through Cross-Lingual Prompt Sensitivity Analysis - Roy J. Burton; Maurice D. Greene; Arthur M. Walters (Computational Intelligence Systems, 2026)
The rapid deployment of multimodal generative models, particularly those capable of producing images from textual prompts, has introduced unprecedented challenges in ensuring cultural fairness across global user populations. This paper proposes a systematic framework for auditing cultural bias in such models through cross-lingual prompt sensitivity analysis, a method that leverages linguistic diversity to expose latent cultural assumptions embedded in model representations. By systematically translating semantically equivalent prompts across languages and assessing the resulting image distributions, we reveal systematic disparities in how models depict culturally specific artifacts, social roles, and geographic settings. The approach emphasizes system-level considerations, including the architectural trade-offs between model scale and bias amplification, the infrastructure required for multilingual evaluation pipelines, and the governance mechanisms needed to operationalize fairness audits. We present a detailed case study involving text-to-image models, demonstrating that even state-of-the-art systems exhibit pronounced cultural gaps that correlate with the linguistic and demographic composition of training data. Our analysis further explores the sustainability of bias mitigation strategies, the interplay between robustness and cultural fidelity, and the policy implications for deploying these models in cross-cultural contexts. The paper concludes with recommendations for integrating cross-lingual auditing into the development lifecycle of generative systems, advocating for a shift from post-hoc evaluation to proactive bias governance.