Sociocultural Problem

While artificial intelligence (AI) translation technology facilitates global communication and information flow, it also brings a series of socio-cultural issues. These issues relate not only to the limitations of the technology itself, but also to the effects and impacts of its application in different social and cultural contexts. The following are the main sociocultural issues caused by AI translation technology:

1.Cultural Misunderstanding and Distortion
When dealing with cross-cultural texts, AI translation systems often struggle to accurately capture linguistic nuances and cultural features, such as slang, idioms, and expressions in specific cultural contexts. This technical limitation may lead to misunderstandings or distortions in the translation results, and sometimes even convey wrong information or cultural images, which in turn affects the real communication between the two languages and cultures.

2.The Risk of Cultural Homogenization
With the acceleration of globalization, the widespread application of AI translation technology may lead to the homogenization of languages and cultures. AI systems tend to translate with the most common and widely recognized uses of language, which can downplay or ignore the uniqueness of local and minority languages. Over time, unique local cultures and expressions may be marginalized, to the detriment of cultural diversity.

3.The Survival Challenge of Language
In the language services market dominated by AI translation technology, resources are focused on mainstream languages and mass markets. This results in insufficient technical support and development opportunities for resource-scarce languages, exacerbating the risk of extinction of these languages. For those languages with fewer speakers, without enough data to support the learning of AI systems, they may ultimately struggle to survive in the digital age.

4.The Strengthening of Prejudice and Discrimination
The learning and output of an AI translation system depend on its training data. If there are gender, race, or cultural biases in the training data, these biases can be learned by the AI system and replicated and reinforced in future translation practices. The spread of such prejudice may not only affect the fair treatment of specific groups, but may also create division and mistrust at a broader socio-cultural level.

Solving these socio-cultural problems requires the joint efforts of technology developers, policy makers and cultural workers. By improving AI technology, making it more sensitive and adaptable to cultural differences, while promoting diversity and inclusion development strategies at the policy level, we can minimize the negative socio-cultural impact of AI translation technology and promote its more positive role in global communication.