A Comparative Study of Deep Learning Models for Bias Detection in Bangla Job Advertisements

Job advertisements often contain explicit or implicit discrimination that reinforces inequality in recruitment activities. While prior research has focused mainly on English texts, no systematic attempt has been made to study bias in Bangla job postings, which play a critical role in shaping labor-market access in Bangladesh. This study proposes a deep learning model to identify and categorize prejudices in the advertisements of jobs in Bangla language. A manually annotated corpus of 1536 sentences based on 200 job circulars was prepared and it is divided into five bias categories: masculine, feminine, age, experience and religious. Six architectures were considered, namely recurrent neural networks (LSTM, BiLSTM, GRU) and transformer-based ones (BanglaBERT, mBERT, XLM-RoBERTa). It is found that the transformer-based models significantly beat recurrent models, with MBERT showing the best results as far as test accuracy of 93.51% and F 1 -macro of 93.71% and minimum Hamming loss of 0.0138. The strength of the models in all categories of bias was also further validated by class-wise analysis. The results indicate that multilingual and language-specific transformer models are effective at detecting discriminatory patterns in Bengali advertisements and offering practical applications for fair recruitment monitoring and policy enforcement.