Mitigating Cultural and Demographic Bias in Federated Advertising Models: A Fairness-Aware Framework for Global Social Commerce Recommendation Systems
Keywords:
federated learning, fairness, cultural bias, demographic bias, social commerce, recommendation systems, advertising, privacy, accountability, socio-technical systemsAbstract
The rapid expansion of global social commerce has intensified the deployment of federated advertising models that operate across heterogeneous cultural and demographic landscapes. While federated learning offers privacy-preserving advantages by keeping user data decentralized, it also introduces significant challenges related to fairness, as local data distributions often reflect deep-seated cultural biases and demographic disparities. This paper presents a fairness-aware framework designed to mitigate such biases in federated recommendation systems for social commerce. The framework integrates three core components: a culturally adaptive weighting mechanism that adjusts for regional consumption norms, a demographic-aware aggregation protocol that prevents majority group dominance, and a dual-loop accountability architecture that enables continuous fairness auditing across clients. We examine structural trade-offs between privacy, accuracy, and fairness, and discuss the implications of deploying such systems within existing socio-technical infrastructures. The analysis extends to governance models, policy alignment, and the role of differential privacy budgets in protecting sensitive demographic attributes. Through a comparative evaluation with baseline federated averaging approaches and centralized advertising models, we demonstrate that the proposed framework reduces measured fairness violations by up to 40% while maintaining competitive recommendation accuracy. The paper concludes by outlining forward-looking strategies for sustainable and ethically robust advertising ecosystems, emphasizing the need for cross-disciplinary collaboration between system architects, cultural theorists, and regulatory bodies.
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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.
