In addition to their ability to learn word meaning from text, embeddings, alas, also reproduce the implicit biases and stereotypes that were latent in the text. As the prior section just showed, embeddings can roughly model relational similarity: 'queen' as the closest word to 'king' -'man' + 'woman' implies the analogy man:woman::king:queen. But these same embedding analogies also exhibit gender stereotypes. For example Bolukbasi et al. (2016) find that the closest occupation to 'man' -'computer programmer' + 'woman' in word2vec embeddings trained on news text is 'homemaker', and that the embeddings similarly suggest the analogy 'father' is to 'doctor' as 'mother' is to 'nurse'. This could result in what Crawford (2017) and Blodgett et al. (2020) call an allocational harm, when a system allo-allocational harm cates resources (jobs or credit) unfairly to different groups. For example algorithms that use embeddings as part of a search for hiring potential programmers or doctors might thus incorrectly downweight documents with women's names. It turns out that embeddings don't just reflect the statistics of their input, but also amplify bias; gendered terms become more gendered in embedding space than they bias amplification were in the input text statistics (Zhao et al. 2017 , Ethayarajh et al. 2019b , Jia et al. 2020 , and biases are more exaggerated than in actual labor employment statistics (Garg et al., 2018) .