The effect of using AI and Google Translate on translating BBC media texts into Arabic

Abstract

Recent post-human translation studies increasingly explore the intricate relationship 
between artificial intelligence (AI) and translation. This paper examines the 
complexities of AI, with a focus on machine translation (MT), and its transformative 
effect on translation in light of technological advancements. Such transformation has 
led to new roles and skills for translators. Our research primarily scrutinizes the style 
and messaging quality of English-Arabic media text translations. We aim to 
illuminate how AI and MT challenge the translation industry, discussing both 
potential benefits and risks for language professionals, and presenting a theoretical 
framework for the changing roles of human translators. This study specifically 
identifies common errors in MT for Arabic-English news texts and vice versa, 
assessing translation quality, fluency, semantic accuracy, and the need for human 
intervention in corrections. We selected purposeful examples from online 
newspapers and used a qualitative method for data analysis, identifying errors based 
on Hsu's (2014) machine translation error classification.