An interactive method for measuring gender bias and evaluating bias in Chinese word embeddings

Chunlin Qin; Xin Zhang; Chaoran Zhou; Yan Liu

doi:10.1117/12.2673321

14 April 2023 An interactive method for measuring gender bias and evaluating bias in Chinese word embeddings

Chunlin Qin, Xin Zhang, Chaoran Zhou, Yan Liu

Proceedings Volume 12613, International Conference on Computer Vision, Application, and Algorithm (CVAA 2022); 126130U (2023) https://doi.org/10.1117/12.2673321
Event: International Conference on Computer Vision, Application, and Algorithm (CVAA 2022), 2022, Chongqing, China

Abstract

Word embedding is widely used in various downstream tasks in the field of Natural Language Processing (NLP). Recent studies reveal that the embedding models trained by corpus contains gender bias in society. In this paper, we propose a new Go/Not-go Embedding Association Test (GNEAT) which is mainly used to analyze gender bias in embedding models by the principle of variance analysis. It is used to calculate the gender bias of a single group of target words other than two groups of words together and also to analyze the interaction between the two groups of target words which is important to analyze effect between different words. In addition, we verify that the projection test, Word Embedding Association Test (WEAT) and clustering analysis method are also applicable in Chinese embedding models and gender bias exist in Chinese embedding models which makes up for the lack of research and calculation of gender bias in Chinese embedding models. The results show that there is gender bias introduced by corpus training in Chinese embedding models. GNEAT method is able to measure the gender bias of a single group of target words and analyze the interaction effect of two groups of target words which is more flexible and comprehensive in measurement.

Citation Download Citation

Chunlin Qin, Xin Zhang, Chaoran Zhou, and Yan Liu "An interactive method for measuring gender bias and evaluating bias in Chinese word embeddings", Proc. SPIE 12613, International Conference on Computer Vision, Application, and Algorithm (CVAA 2022), 126130U (14 April 2023); https://doi.org/10.1117/12.2673321

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