Understanding Gender Bias in Knowledge Base Embeddings

Yupei Du, Qi Zheng, Yuanbin Wu, Man Lan, Yan Yang, Meirong Ma


Abstract
Knowledge base (KB) embeddings have been shown to contain gender biases. In this paper, we study two questions regarding these biases: how to quantify them, and how to trace their origins in KB? Specifically, first, we develop two novel bias measures respectively for a group of person entities and an individual person entity. Evidence of their validity is observed by comparison with real-world census data. Second, we use the influence function to inspect the contribution of each triple in KB to the overall group bias. To exemplify the potential applications of our study, we also present two strategies (by adding and removing KB triples) to mitigate gender biases in KB embeddings.
Anthology ID:
2022.acl-long.98
Volume:
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Month:
May
Year:
2022
Address:
Dublin, Ireland
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1381–1395
Language:
URL:
https://aclanthology.org/2022.acl-long.98
DOI:
10.18653/v1/2022.acl-long.98
Bibkey:
Cite (ACL):
Yupei Du, Qi Zheng, Yuanbin Wu, Man Lan, Yan Yang, and Meirong Ma. 2022. Understanding Gender Bias in Knowledge Base Embeddings. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1381–1395, Dublin, Ireland. Association for Computational Linguistics.
Cite (Informal):
Understanding Gender Bias in Knowledge Base Embeddings (Du et al., ACL 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.acl-long.98.pdf