Federated Auditing of Cultural Fairness in Privacy-Preserving Text-to-Image Generation Models
Keywords:
Federated Learning, Cultural Fairness, Text-to-Image Models, Privacy-Preserving Auditing, Algorithmic Fairness, Decentralized GovernanceAbstract
The rapid proliferation of text-to-image generative models has surfaced deep structural concerns regarding cultural fairness, representation, and inclusion. These models, trained on vast and often uncurated web-scale datasets, exhibit significant cultural biases that risk homogenizing visual expression and marginalizing underrepresented communities. Simultaneously, regulatory frameworks and community expectations increasingly demand rigorous auditing of such systems without compromising the data sovereignty of the cultural groups involved. This paper proposes a federated auditing architecture that enables the systematic evaluation of cultural fairness in text-to-image models while preserving the privacy and local control of culturally sensitive data. We present a system-level analysis of the trade-offs inherent in distributing fairness auditing across heterogeneous cultural nodes, discussing architectural design, secure aggregation protocols, governance structures, and policy alignment. Through a detailed conceptual framework, we examine how federated fairness auditing can reconcile the competing demands of statistical rigor, cultural authenticity, and privacy preservation. The discussion extends to deployment infrastructure, robustness against adversarial manipulation, and the integration of community-defined fairness metrics. By centering the infrastructural and governance dimensions, this work provides a comprehensive pathway toward accountable and culturally aware generative artificial intelligence systems, highlighting the necessity of interdisciplinary collaboration between systems engineers, cultural stakeholders, and policy makers.
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This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
