  Pretrained language models inherently exhibit various social biases,
prompting a crucial examination of their social impact across various
linguistic contexts due to their widespread usage. Previous studies have
provided numerous methods for intrinsic bias measurements, predominantly
focused on high-resource languages. In this work, we aim to extend these
investigations to Bangla, a low-resource language. Specifically, in this study,
we (1) create a dataset for intrinsic gender bias measurement in Bangla, (2)
discuss necessary adaptations to apply existing bias measurement methods for
Bangla, and (3) examine the impact of context length variation on bias
measurement, a factor that has been overlooked in previous studies. Through our
experiments, we demonstrate a clear dependency of bias metrics on context
length, highlighting the need for nuanced considerations in Bangla bias
analysis. We consider our work as a stepping stone for bias measurement in the
Bangla Language and make all of our resources publicly available to support
future research.
