Recently there has been a growing concern about machine bias, where trained
statistical models grow to reflect controversial societal asymmetries, such as
gender or racial bias. A significant number of AI tools have recently been
suggested to be harmfully biased towards some minority, with reports of racist
criminal behavior predictors, Iphone X failing to differentiate between two
Asian people and Google photos' mistakenly classifying black people as
gorillas. Although a systematic study of such biases can be difficult, we
believe that automated translation tools can be exploited through gender
neutral languages to yield a window into the phenomenon of gender bias in AI.
In this paper, we start with a comprehensive list of job positions from the
U.S. Bureau of Labor Statistics (BLS) and used it to build sentences in
constructions like "He/She is an Engineer" in 12 different gender neutral
languages such as Hungarian, Chinese, Yoruba, and several others. We translate
these sentences into English using the Google Translate API, and collect
statistics about the frequency of female, male and gender-neutral pronouns in
the translated output. We show that GT exhibits a strong tendency towards male
defaults, in particular for fields linked to unbalanced gender distribution
such as STEM jobs. We ran these statistics against BLS' data for the frequency
of female participation in each job position, showing that GT fails to
reproduce a real-world distribution of female workers. We provide experimental
evidence that even if one does not expect in principle a 50:50 pronominal
gender distribution, GT yields male defaults much more frequently than what
would be expected from demographic data alone.
We are hopeful that this work will ignite a debate about the need to augment
current statistical translation tools with debiasing techniques which can
already be found in the scientific literature.

本文研究了机器偏见和性别偏见，使用性别中性语言对 Google 翻译进行了实验，结果显示翻译的默认性别更偏向于男性，特别是在 STEM 领域中，这种偏见程度远超实际分布，因此需要对当前的统计翻译工具进行偏误纠正。