deb2viz: Debiasing gender in word embedding data using subspace visualization

By Enoch Opanin Gyamfi, Yunbo Rao, Miao Gou, Yanhua Shao. 2020

8 citations recorded by the source corpus.

Word embedding have been used in numerous Natural Language Processing and Machine Learning tasks. However, it is a high-dimensional vector field that propagate stereotypes to software applications. Its current debiasing frameworks do not completely capture its embedded patterns. In this paper, we propose deb2viz, a visual debiasing approach that explores and manipulates the high-dimensional patterns of word embedding field. First, we partition this vector field into interrelated low-dimensional subspaces to equalize and neutralize distances between gender-definitional and gender-neutral words. To further reduce gender bias, we update the distances of appropriate nearest neighbors for gender-neutral words to be arbitrarily close. Experimental results on several benchmark standards show the competitiveness of our proposed method in mitigating bias within pre-trained word2vec embedding model.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Safety, Robustness & Interpretability

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Citations in the corpus

Language (Technology) is Power: A Critical Survey of “Bias” in NLP (2020, background): “…Stanovsky et al. (2019); Sweeney and Najafian (2019); Vaidya et al. (2019); Zhiltsova et al. (2019); Chopra et al. (2020); Gonen and Webster (2020); Gyamfi et al. (2020); Hube et al. (2020); Ravfogel et al. (2020); Rios (2020); Ross et al. (2020); Saunders and Byrne (2020); Sen and Ganguly…”
Theories of “Gender” in NLP Bias Research (2022, background): “[104] Enoch Opanin Gyamfi, Yunbo Rao, Miao Gou, and Yanhua Shao.”