How AI and machine learning are making news media more accessible
Year: 2025
Type: article
Abstract: The digital revolution has fundamentally transformed how news is produced and consumed, yet accessibility barriers persist for specific demographics including individuals with disabilities, non-native language speakers, and those with limited time or cognitive bandwidth. Artificial intelligence and machine learning technologies are now bridging these gaps through three key innovations: automatic content summarization, real-time translation, and AI-generated voice narration. These technologies democratize access to information across previously underserved populations, with neural network-based accessibility solutions now deployed across major global news outlets. This article explores the technical underpinnings of these AI-driven solutions revolutionizing accessibility in news media, from the extractive and abstractive summarization approaches to sophisticated neural machine translation architectures and modern text-to-speech systems. The integration of these technologies into unified content pipelines with API-driven microservices enables comprehensive accessibility transformations, while emerging directions like multimodal understanding and personalized content adaptation promise to further enhance news accessibility despite ongoing ethical and technical challenges.
Author: Ramachandra, Prerna
Institution: Yahoo (United States)
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7084768985
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