Assessing Gender Bias in Machine Translation -- A Case Study with Google Translate

Understand

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.