Keywords
Abstract
This research focuses on the effect of artificial intelligence technologies on the editing and communication of news photographs, exploring the automation of visual journalism workflows in contemporary newsrooms. Investigating the use of automated image enhancement, content-aware editing, intelligent cropping, and quality assessment via deep learning reveals significant efficiency gains of 45-60% in speed relative to processing editorial benchmarks. The study shows that tailored AI distribution models employing personalized recommendation systems, cross-platform adaptation, and audience analytics for predictive modelling have changed paradigms of engagement with media content, attaining viral probability assessment of 0.73 AUC and cross-platform consistency metrics of 0.92. Solving critical problems of misinformation juxtaposed with efficient archival system management, AI-powered advanced computer vision real-time authenticity verification and automated metadata generation tackle essential challenges. The results suggest that operational automation of processes shifts the boundaries of photojournalistic practice to new levels, invoking complex considerations about authorship, creative agency, identity, and the role of the professional in automated systems. The integration of algorithmic machine learning tools into conventional news editing workflows forms advanced human-AI collaborative systems that preserve ethical journalism while maintaining technical efficiency. This indicates a fundamental change towards algorithm-based mediation in the production of video news, although stubborn issues of bias circumvention, ethical transparency, and documentary fidelity in the age of pervasive synthetic media remain.
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