Replacing the Irreplaceable: A Case Study on the Limitations of MT and AI Translation during the 2023 G aza- I srael Conflict (2025)

Abstract: Despite the remarkable development of artificial intelligence (AI) and machine translation (MT) in recent years, which has made them more efficient, less costly and easier to navigate, they still struggle to match the abilities of human translators. The limitations shown by AI and MT, which have been detected in various domain-specific texts and contexts, sustain the debate over whether they can fully replace human translators. Nevertheless, very few studies have examined the translation abilities of AI and MT during conflicts and high-stakes contexts. This paper explores some of these limitations that were detected during the 2023 Gaza-Israel conflict, illustrating significant examples from X (formerly Twitter). These examples showcase limitations in 1) translating cultural references, 2) avoiding critical errors in high-stakes context, 3) preventing bias and intervention, and 4) translating cursive handwriting. This is done through a combination of descriptive, comparative and experimental analysis methods, highlighting risks and implications associated with using these tools in such sensitive contexts, while contributing to the broader discussion on whether advances in AI and MT will diminish the need for human translators.

## introduction advances in translation technologies have made it easier, quicker and cheaper to translate different types of text for a wide range of users. however, despite all the significant developments in recent years, artificial intelligence (ai) and machine translation (mt) still face challenges in replicating human abilities. these challenges continue to fuel the debate over whether they can take the place of human translators in the near future. although limitations of ai and mt have been explored across various domain-specific texts and contexts, very little research has been done on their limitations in the political domain, specifically during conflicts. this paper explores some of the limitations that were encountered during the 2023 gaza-israel conflict, illustrating significant examples from x (formerly twitter) in four different key areas. the study employs a combination of descriptive, comparative, and experimental analysis methods to provide a comprehensive investigation into the limitations of text, image and audiovisual translation. since this study focuses on a single conflict, the examples provided are not intended to be exhaustive. nonetheless, they effectively illustrate the limitations of ai and mt and merit further discussion for several reasons: (1) they highlight the risks associated with relying on such tools in conflicts and high-stakes contexts; (2) they help pinpoint specific areas where ai and mt require further refinement; and (3) they contribute to the ongoing debate about whether advancements in ai and mt will reduce the demand for human translators. ## methodologies during the 2023 gaza-israel conflict, users on x utilized ai tools to translate videos shared by other users from both sides of the conflict. these tools included ezdubs, an ai-powered tool designed to dub videos effortlessly from and into various languages, and translatemom, an ai-powered tool designed to caption videos from and into various languages. both tools operate through bots specifically designed to translate videos on multiple platforms, including x. in addition, users relied on the translation tool integrated into x and powered by google translate 1 , to translate texts posted on x during this conflict. google translate is a well-known online service that can translate text in over 100 languages, and is listed in g2.com as the top machine translation system 2 . in this study, the performance of the ai tools ezdubs and translatemom is examined, as well as the abilities of google translate. these include the ability to translate text via the integrated feature on x, which allows users to instantly translate posts and comments within the platform, and the ability to translate text embedded in images by using the "camera translation" feature, which enables users to capture a photo of text and translate it instantly. the dataset was selected after examining hundreds of mt and ai translations shared by x users during the conflict. particular emphasis was placed on translations that met the following criteria: (1) they generated controversy or public outrage; (2) they were widely circulated or featured in prominent hashtags; or (3) they were actively contested through user comments or critically addressed by news outlets. with the assistance of two bilingual arabic-english translators and two bilingual hebrew-english translators with no less than five years of experience, the accuracy of these translations was examined, and only materials that were conclusively identified as inaccurate and containing errors were explored in this study. the concept of accuracy in this context refers to the degree of correctness and fidelity to the source text (molina and albir 2002). the study integrates descriptive, comparative and experimental analyses, showcasing four different limitations of ai and mt. the term 'limitation' is used in this study to encompass not only the failures of mt and ai, but also their inherent constraints, including instances of human intervention and text manipulation, as can be seen in section 4.3. the descriptive analysis includes highlighting errors in the translations, analysing the nature of these errors and explaining the circumstances of their delivery. the comparative analysis compares ai and mt performance in translating some of these encounters against reference translations provided by professional arabic and hebrew translators, in order to highlight errors and differences in accuracy. lastly, due to instances where translation technologies were evidently used and resulted in errors, but the specific tools employed were not identified, a systematic experimental analysis was conducted to investigate these issues rigorously using a well-documented tool, namely google translate, as can be seen in section 4.4. ## conclusion although ai and mt are improving significantly, they still have limitations that make them unreliable, and even too risky to trust at times. this paper highlighted some of these limitations in the political field, specifically during conflicts and high-stakes contexts. such limitations appeared in translating cultural references and cursive handwriting, as well as the inability to avoid errors at critical times and a susceptibility to bias and intervention. these limitations should serve as evidence that human translators are indispensable, especially in situations where translation tools are unable to fully and accurately translate the content, and that relying on translation tools is a risk that should not be taken in conflicts and high-stakes contexts. there is a reason such tools require postediting carried out by humans, especially when errors in translation can cause unrepairable damage. it would be best, moving forward, to balance the two; translation technologies with all their abilities to translate large amount of text at speed, and human translators with all their intelligence and comprehension abilities. additionally, the limitations of such tools and best ways to use them need to be clarified for their users. it is essential to raise public awareness regarding their propensity for error and bias, especially in light of the evolving state of ai. continued research that builds upon the limitations outlined in this study is essential for advancing mt and ai. these technologies must draw on such findings to refine their performance and ensure more accurate and appropriate outputs. more research is also needed to understand the nature of the risks imposed when such tools are used during conflicts and high-stakes contexts. lastly, further research is warranted not only on the limitations and failures of mt and ai, but also on issues related to fact-checking and the potential for data manipulation.