AI-Driven Multicultural Identity Preservation

Abstract

The global expansion of Artificial Intelligence (AI) has highlighted significant challenges in inclusivity and representation, particularly for underrepresented communities. Current AI systems often fail to accommodate diverse linguistic and cultural contexts, resulting in biases in name pronunciation, language preservation, and communication. This research proposes a framework for advancing inclusivity in AI through Natural Language Processing (NLP) and Reinforcement Learning (RL). The envisioned system could integrate with home assistants like Siri and Alexa, enabling real-time interactions in local languages while maintaining cultural relevance. Key proposed features include accurate pronunciation of names, conversational capabilities in underrepresented languages, and an interactive platform where users can learn their language, history, and cultural heritage. By leveraging transformer-based models and adaptive RL frameworks, this research aims to explore solutions that bridge the gap in AI inclusivity for low-resource languages and culturally diverse populations.