Exploring Human Gender Stereotypes with Word Association Test

By Yupei Du, Yuanbin Wu, Man Lan. 2019

27 citations recorded by the source corpus.

Word embeddings have been widely used to study gender stereotypes in texts. One key problem regarding existing bias scores is to evaluate their validities: do they really reflect true bias levels? For a small set of words (e.g. occupations), we can rely on human annotations or external data. However, for most words, evaluating the correctness of them is still an open problem. In this work, we utilize word association test, which contains rich types of word connections annotated by human participants, to explore how gender stereotypes spread within our minds. Specifically, we use random walk on word association graph to derive bias scores for a large amount of words. Experiments show that these bias scores correlate well with bias in the real world. More importantly, comparing with word-embedding-based bias scores, it provides a different perspective on gender stereotypes in words.

Subfields: Artificial Intelligence, General Social Sciences, Natural Language Processing, Safety, Robustness & Interpretability

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Citations in the corpus

Queens Are Powerful Too: Mitigating Gender Bias in Dialogue Generation (2019, background): “…disparities and harmful biases in algorithms and text (Cao and Daum´e III 2020; Chang et al. 2019; Chang and McKeown 2019; Costa-juss ` a 2019; Du et al. 2019; Emami et al. 2019; Garimella et al. 2019; Gaut et al. 2020; Habash et al. 2019; Hashempour 2019; Hoyle et al. 2019; Lee et al. 2019a;…”