  The influence of Large Language Models (LLMs) is rapidly growing, automating
more jobs over time. Assessing the fairness of LLMs is crucial due to their
expanding impact. Studies reveal the reflection of societal norms and biases in
LLMs, which creates a risk of propagating societal stereotypes in downstream
tasks. Many studies on bias in LLMs focus on gender bias in various NLP
applications. However, there's a gap in research on bias in emotional
attributes, despite the close societal link between emotion and gender. This
gap is even larger for low-resource languages like Bangla. Historically, women
are associated with emotions like empathy, fear, and guilt, while men are
linked to anger, bravado, and authority. This pattern reflects societal norms
in Bangla-speaking regions. We offer the first thorough investigation of
gendered emotion attribution in Bangla for both closed and open source LLMs in
this work. Our aim is to elucidate the intricate societal relationship between
gender and emotion specifically within the context of Bangla. We have been
successful in showing the existence of gender bias in the context of emotions
in Bangla through analytical methods and also show how emotion attribution
changes on the basis of gendered role selection in LLMs. All of our resources
including code and data are made publicly available to support future research
on Bangla NLP.
  Warning: This paper contains explicit stereotypical statements that many may
find offensive.
