Exploring Religions and Cross-Cultural Sensitivities in Conversational AI

With the fast growth of conversational AI, it is very important that these systems can handle religion and cultural sensitivity correctly. New, cutting-edge models like BERT, mBERT (multilingual BERT), and XLM-RoBERTa are very good at many types of natural language processing. But not a lot is known about how well they talk and act about their religious views. The dataset primarily contains content related to Christianity, Islam, and Hinduism, reflecting their prevalence in online discussions and AI training corpora. AIs need to learn how to deal with harsh words, social nuances, and touchy topics where they can talk about religion. Artificial intelligence systems could give answers that don't seem to be legit or different meaning which are very sensitive. This is a big ethical problem.We look at how well BERT, mBERT, and XLM-RoBERTa can handle religious and cultural situations in this study. We focus on how well they do across languages using the PAWS-X dataset. PAWS-X is a good tool for testing cross-lingual transfer because it has goals for finding paraphrases in a number of different languages. This lets us see how well these models understand complex meanings in a variety of linguistic and cultural settings. We use the dataset to test how well the model works in talks about Hinduism, Islam, and Christianity, which are very sensitive to cultural and religious differences.To see how well these models can understand underlying cultural differences and come up with suitable responses, we test them on religious sentence pairs as part of our methodology. We test both their language skills and their ability to stay sensitive when religious or doctrinal information is present. We also look into their cross-lingual transfer skills to see how well they work in different religious meaning. BERT, mBERT, and XLM-RoBERTa perform well in basic tasks but struggle with religious and cultural nuances. BERT and mBERT handle multiple languages effectively but often falter in unclear religious contexts, especially when switching between languages with unique religious terms.In this study, we assess sensitivity by analyzing model responses for their ability to correctly interpret religious connotations, avoid offensive language, and acknowledge cultural context. We apply sentiment analysis, contextual relevance scoring, and manual validation from domain experts to measure how well these models handle sensitive topics.