An analysis of 630 billion words published online suggests that people tend to think of men when using gender-neutral terms, a sexist bias that could be learned by AI models
at New York University and colleagues used a statistical algorithm to analyse a collection of 630 billion words contained within 2.96 billion web pages gathered in 2017, including informal text from blogs and discussion forums as well as more formal text written by the media, corporations and governments, mostly in English. They used an approach called word embedding which derives the intended meaning of a word by the frequency it occurs in context with other words.
They found that words like “person”, “people” and “humanity” are used in contexts that better match the context of words like “men”, “he” and “male” than those of words like “women”, “she” and “her”. The team says that because these gender-inclusive words were used more similarly to those that refer to men, people may see them as more male in their conceptual meaning – a reflection of male-dominated society.
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