Semantics derived automatically from language corpora contain human-like biases
The paper and title implies it&#x27;s absorbing these stereotypes from humans. I think there is another explanation. Remember these models are trained on a dataset of news or Wikipedia articles. And it&#x27;s &#x27;goal&#x27; is to find vectors that predict what contexts words are more likely to appear in.<p>So if 34% of doctors are female, then you would expect 34% of doctors in news or Wikipedia articles to be female. Even if the articles are completely unbiased and the writers have no stereotypes whatsoever. And so the word vector would naturally label &quot;doctor&quot; something like &quot;66% likely to occur in a male context&quot;.<p>And in fact this paper confirms that. Figure 1 shows that the word vectors are highly predictive of the actual gender distribution of various occupations. Probably much more accurate than most people would be. So it&#x27;s not mindlessly absorbing human stereotypes. It&#x27;s learning reality&#x27;s stereotypes.<p>This result is completely expected and desirable. What makes word vectors so powerful is how they can learn complicated correlations between words and their contexts. The famous example is how it learns that &quot;Queen&quot; is the female equivalent of &quot;King&quot;. Which is a gender stereotype as well. If it wasn&#x27;t able to learn that doctors were a bit more likely to be male, that would be more surprising.