  This chapter focuses on gender-related errors in machine translation (MT) in
the context of low-resource languages. We begin by explaining what low-resource
languages are, examining the inseparable social and computational factors that
create such linguistic hierarchies. We demonstrate through a case study of our
mother tongue Bengali, a global language spoken by almost 300 million people
but still classified as low-resource, how gender is assumed and inferred in
translations to and from the high(est)-resource English when no such
information is provided in source texts. We discuss the postcolonial and
societal impacts of such errors leading to linguistic erasure and
representational harms, and conclude by discussing potential solutions towards
uplifting languages by providing them more agency in MT conversations.
