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.

本文研究了机器翻译 (MT) 中的偏见问题。通过使用性别中立的土耳其语和性别化的英语，本文探讨了MT模型中明显和潜在的性别偏见现象。研究者提出了一种方法来研究不对称性别标记，并通过评估人格化职业和人格刻板印象的表达方式揭示了MT模型中的偏见问题。本文强调了MT模型开发需要更多具有跨学科背景的语言详细分析的研究。

研究隐性性别偏见:土耳其语和英语机器翻译模型的案例研究