  As Machine Translation (MT) has become increasingly more powerful,
accessible, and widespread, the potential for the perpetuation of bias has
grown alongside its advances. While overt indicators of bias have been studied
in machine translation, we argue that covert biases expose a problem that is
further entrenched. Through the use of the gender-neutral language Turkish and
the gendered language English, we examine cases of both overt and covert gender
bias in MT models. Specifically, we introduce a method to investigate
asymmetrical gender markings. We also assess bias in the attribution of
personhood and examine occupational and personality stereotypes through overt
bias indicators in MT models. Our work explores a deeper layer of bias in MT
models and demonstrates the continued need for language-specific,
interdisciplinary methodology in MT model development.
