CHALLENGES IN DEVELOPING MULTILINGUAL AI SYSTEMS: TECHNICAL, LINGUISTIC, AND ETHICAL PERSPECTIVES
Keywords:
Multilingual AI, low-resource languages, natural language processing, linguistic diversity, bias in AI.Abstract
The development of multilingual Artificial Intelligence (AI) systems has significantly advanced over the past decade, enabling global communication, real-time translation, and inclusive digital services. However, achieving high-quality performance across languages presents persistent challenges. This paper examines the technical, linguistic, and ethical barriers to building effective multilingual AI models. Technical issues include limited training data for low-resource languages, performance disparities, and the high computational cost of model training. Linguistic challenges arise from morphological complexity, idiomatic usage, and cultural variations in meaning. Ethical considerations involve bias, fairness, and the risk of excluding minority languages. Possible solutions, such as transfer learning, data augmentation, and community-driven corpus creation, are proposed to address these challenges.
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