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Language diversity presents challenges to effective communication in an interconnected world, necessitating solutions to bridge linguistic divides and foster equitable access to information. The rise of AI-powered translation systems offers promise in addressing these challenges by leveraging advanced algorithms and neural networks to automate language translation. However, concerns arise regarding their impact on communication dynamics and cultural preservation. This paper examines the multifaceted impacts of AI-powered translation systems on communication equity and inclusion, drawing on interdisciplinary insights. It explores the historical evolution of AI translation technologies, their operational functionalities, and the challenges they face in accommodating linguistic minorities and addressing biases. Strategies to enhance AI adaptability, such as text-guided domain adaptation and human-AI collaboration, are discussed, along with recommendations for policymakers, developers, and practitioners to promote inclusive communication practices. Despite significant progress, challenges remain, including accuracy, bias, and ethical considerations. Future research should focus on developing robust translation models for low-resource languages, mitigating biases, and enhancing usability for diverse communication needs.
Zaki et al. (Sun,) studied this question.