Multilingual Embeddings and Retrieval Fairness in Cross-Border Education Platforms

Abstract

The rapid expansion of cross border education platforms has democratized access to learning resources, yet it has also exposed significant disparities in how educational content is retrieved for users across different linguistic demographics. As these platforms increasingly rely on multilingual embeddings to power semantic search and content recommendation, understanding the fairness of these retrieval systems becomes a critical imperative. This paper investigates the intricate relationship between multilingual embedding alignments and retrieval fairness through the lens of causal modeling. By transitioning from traditional correlational analysis to a structural causal framework, we systematically identify and isolate the confounding variables that distort retrieval outcomes for non dominant languages. The study develops a comprehensive theoretical model that maps the causal pathways from input query languages to final retrieval rankings, demonstrating how latent biases in pre-trained language models disproportionately disadvantage learners from underrepresented linguistic backgrounds. Leveraging a rich dataset constructed from simulated cross border educational interactions, we apply counterfactual inference techniques to estimate the true causal effect of language representation on retrieval utility. Our findings reveal that standard multilingual alignment techniques often exacerbate representational harms, resulting in systematic ranking degradation for queries in low resource languages. Furthermore, the application of causal interventions significantly mitigates these disparities, offering a robust methodological foundation for developing more equitable information retrieval systems in global educational contexts.