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Google Translate’s gender bias pairs “he” with “hardworking” and “she” with lazy, and other examples

By Nikhil Sonnad
Published

In the Turkish language, there is one pronoun, “o,” that covers every kind of singular third person. Whether it’s a he, a she, or an it, it’s an “o.” That’s not the case in English. So when Google Translate goes from Turkish to English, it just has to guess whether “o” means he, she, or it. And those translations reveal the algorithm’s gender bias.

Here is a poem written by Google Translate on the topic of gender. It is the result of translating Turkish sentences using the gender-neutral “o” to English (and inspired by this Facebook post).

Gender


by Google Translate

he is a soldier


she’s a teacher


he is a doctor


she is a nurse

he is a writer


he is a dog


she is a nanny


it is a cat

he is a president


he is an entrepreneur


she is a singer


he is a student


he is a translator

he is hard working


she is lazy

he is a painter


he is a hairdresser


he is a waiter


he is an engineer


he is an architect


he is an artist


he is a secretary


he is a dentist


he is a florist


he is an accountant


he is a baker


he is a lawyer


he is a belly dancer

he-she is a police

she is beautiful


he is very beautiful


it’s ugly


it is small


he is old

he is strong


he is weak


he is pessimistic


she is optimistic

It’s not just Turkish. In written Chinese, the pronoun 他 is used for “he,” but also when the person’s gender is unknown, like “they” has come to be used in English. But Google only translates into “she” when you use 她, the pronoun that specifically identifies the person as a woman. So in the case of a gender tie, Google always chooses “he.” In Finnish, the pronoun “hän,” meaning either “he” or “she,” is rendered as “he.”

In a way, this is not Google’s fault. The algorithm is basing its translations on a huge corpus of human language, so it is merely reflecting a bias that already exists. In Estonian, Google Translate converts “[he/she] is a doctor” to “she,” so perhaps there is less cultural bias in that corpus.

At the same time, automation can reinforce biases, by making them readily available and giving them an air of mathematical precision. And some of these examples might not be the most common that Turks look to translate into English, but regardless, the algorithm has to make a decision as to “he” or “she.”

At least she remains optimistic.

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