| Challenge: | Knowledge base (KB) embeddings have been shown to contain gender biases . authors develop two new bias measures to quantify them and trace their origins in KB . |
| Approach: | They propose two ways to quantify gender biases in knowledge base (KB) embeddings . they use the influence function to inspect the contribution of each triple in KB to the overall group bias . |
| Outcome: | The proposed measures are compared with real-world census data to examine gender biases. |
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Robustness and Reliability of Gender Bias Assessment in Word Embeddings: The Role of Base Pairs (2020.aacl-main)
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| Challenge: | Existing methods to quantify gender bias in word embeddings are not robust and cannot identify common types of bias. |
| Approach: | They propose to quantify gender bias by using cosine similarity to a pair of gender words and using analogies. |
| Outcome: | The proposed methods are not robust and cannot identify common types of bias, while analogies are unsuitable indicators. |
Debiasing knowledge graph embeddings (2020.emnlp-main)
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| Challenge: | Existing methods to train knowledge graph embeddings to be neutral to sensitive attributes such as gender have been shown to increase training time by a factor of eight or more. |
| Approach: | They propose a method where all embeddings are trained to be neutral to sensitive attributes such as gender by default using an adversarial loss. |
| Outcome: | The proposed method reduces training time by eightfold and improves accuracy. |
Lipstick on a Pig: Debiasing Methods Cover up Systematic Gender Biases in Word Embeddings But do not Remove Them (N19-1)
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| Challenge: | Existing methods to remove gender bias from word embeddings are insufficient, we argue . existing methods for gender-neutral modeling are ineffective, we conclude . |
| Approach: | They propose methods to reduce gender bias in word embeddings by debiasing them using text corpora. |
| Outcome: | The proposed methods show that they can reduce gender bias in word embeddings . the proposed methods are insufficient and should not be trusted, the authors argue . |
Gender Bias in Meta-Embeddings (2022.findings-emnlp)
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| Challenge: | Existing methods to develop meta-embeddings from source embeddings contain unfair gender-related biases, and how these influence the meta-bedding has not been studied yet. |
| Approach: | They propose to use multiple debiasing methods on a single source embedding to create a gender-based meta-embedding. |
| Outcome: | The proposed method amplifies gender biases compared to input source embeddings. |
Exploring the Linear Subspace Hypothesis in Gender Bias Mitigation (2020.emnlp-main)
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| Challenge: | Existing methods for gender bias mitigation for word embeddings are based on pre-trained word embeds . however, the assumption that the bias subspace is linear is untested . |
| Approach: | They propose a method to isolate gender bias in word embeddings using pre-trained word embeds. |
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Identifying and Reducing Gender Bias in Word-Level Language Models (N19-3)
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| Challenge: | Existing discriminatory biases in training data can be amplified by models . text corpora exhibit socially problematic biase . |
| Approach: | They propose a metric to measure gender bias and a regularization loss term to minimize embeddings onto an embeddable subspace that encodes gender. |
| Outcome: | The proposed method reduces gender bias up to an optimal weight assigned to the loss term, and the model becomes unstable as the perplexity increases. |
Gender Bias in Multilingual Embeddings and Cross-Lingual Transfer (2020.acl-main)
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| Challenge: | Multilingual word embeddings embed words from many languages into a single semantic space such that words with similar meanings are close to each other regardless of the language. |
| Approach: | They propose to use multilingual word embeddings to align embeddable words from multiple languages into a single semantic space so that words with similar meanings are close to each other regardless of the language. |
| Outcome: | The proposed model can be used to learn gender bias in multilingual representations and to improve transfer learning. |
Exploring Human Gender Stereotypes with Word Association Test (D19-1)
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| Challenge: | Existing word embeddings have been used to study gender stereotypes in texts . however, evaluating their validities is still an open problem . et al.: this study investigates gender bias using the lens of language, especially, the words . |
| Approach: | They use word association test to derive bias scores for large amount of words . they find that these bias scores correlate well with bias in the real world . |
| Outcome: | The proposed method correlates well with bias in the real world, and with census data, it provides a different perspective on gender stereotypes in words. |
When do Word Embeddings Accurately Reflect Surveys on our Beliefs About People? (2020.acl-main)
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| Challenge: | a study of word embeddings shows that social biases are more accurate than survey data for some dimensions of meaning. |
| Approach: | a new study investigates the extent to which word embeddings accurately reflect biases . they find that biased word embeds mirror survey data across 17 dimensions of social meaning . |
| Outcome: | a new study shows that word embeddings accurately reflect biases on average across dimensions of social meaning . biased embedders are more reflective of survey data for some dimensions of meaning than others, the study finds . |
GKnow: Measuring the Entanglement of Gender Bias and Factual Gender (2026.acl-long)
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| Challenge: | Recent studies have focused on mitigating gender bias, but mechanistic interpretations of gender fail to distinguish between factually gendered outputs and gender biased outputs. |
| Approach: | They propose a benchmark to assess gender knowledge and gender bias in language models . they use neuron ablation to disentangle stereotypical and factual gender . |
| Outcome: | The proposed benchmark assesses gender knowledge and gender bias in language models across different types of gender-related predictions. |