Application of AI algorithms in detecting cultural bias in English writing

This paper studies the role of AI algorithms in detecting cultural bias in English writing, and proposes an improved model based on the Transformer architecture. By introducing a multi-layer cultural feature attention mechanism, it achieves accurate identification of bias types such as stereotypes, value orientations, and semantic biases and their subcategories. The experimental results show that the improved model performs well in the bias detection task, with an accuracy of 89.4% and an F1 score of 87.4%, which is significantly better than the baseline model. The study further reveals the influence of cultural background and style on the distribution of bias patterns. Regional stereotypes are more prominent in native speaker texts, individualistic tendencies dominate in non-native speaker texts, and social media texts show greater variability. Through the attention weight heat map and the deviation subcategory distribution box plot, the semantic understanding ability and cross-cultural differences of the model can be visualized, providing technical support for cross-cultural writing education and cultural sensitivity research.