Assessing Gender Bias of Pretrained Bangla Language Models in STEM and SHAPE Fields

AbstractGender bias continues to shape societal perceptions across both STEM (Science, Technology, Engineering, and Mathematics) and SHAPE (Social Sciences, Humanities, and the Arts for People and the Economy) domains. While existing studies have explored such biases in English language models, similar analyses in Bangla—spoken by over 240 million people—remain scarce. In this work, we investigate gender-profession associations in Bangla language models. We introduce Pokkhopat, a curated dataset of gendered terms and profession-related words across STEM and SHAPE disciplines. Using a suite of embedding-based bias detection methods—including WEAT, ECT, RND, RIPA, and cosine similarity visualizations—we evaluate 11 Bangla language models. Our findings show that several widely-used open-source Bangla NLP models (e.g., sagorsarker/bangla-bert-base) exhibit significant gender bias, underscoring the need for more inclusive and bias-aware development in low-resource languages like Bangla. We also find that many STEM and SHAPE-related words are absent from these models’ vocabularies, complicating bias detection and possibly amplifying existing biases. This emphasizes the importance of incorporating more diverse and comprehensive training data to mitigate such biases moving forward. Code available at https://github.com/HerWILL-Inc/ACL-2025/.