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. |
| Outcome: | The proposed method eliminates gender bias in word embeddings but assumes bias subspace is linear . the proposed method has some drawbacks, but it is a good one for a non-linear analysis. |
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| Challenge: | Existing methods to debias word embeddings from human-generated corpora inherit strong gender bias . prior work has suggested removing gender component from pre-trained word embeds or compressing gender information into a few dimensions of the embeddable space . |
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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. |
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Neutralizing Gender Bias in Word Embeddings with Latent Disentanglement and Counterfactual Generation (2020.findings-emnlp)
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| Challenge: | Recent research shows word embeddings have strong gender biases in embeddable spaces . a proposed method can be used to debiase word embeds without loss of semantic information . |
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How Far Can It Go? On Intrinsic Gender Bias Mitigation for Text Classification (2023.eacl-main)
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| Challenge: | a growing interest in exploring how gender bias pertains in contextualized language models has been generated . intrinsic mitigation strategies and bias metrics have been proposed to mitigate gender bias in contextualised language models . |
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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 . |
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Mitigating Gender Bias in Natural Language Processing: Literature Review (P19-1)
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Tony Sun, Andrew Gaut, Shirlyn Tang, Yuxin Huang, Mai ElSherief, Jieyu Zhao, Diba Mirza, Elizabeth Belding, Kai-Wei Chang, William Yang Wang
| Challenge: | NLP models propagate and may even amplify gender bias found in text corpora . methods to mitigate gender bias in NLP are relatively nascent . |
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Unpacking Bias: An Empirical Study of Bias Measurement Metrics, Mitigation Algorithms, and Their Interactions (2024.lrec-main)
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| Challenge: | Word embeddings (WE) models reflect gender, racial, and religious stereotypes from the corpus on which they are trained. |
| Approach: | They propose a method that carefully controls for word sets and vector normalization to address these factors. |
| Outcome: | The proposed method detects consistency between different mitigation methods and the evaluation words used by the mitigation methods. |
Gender-preserving Debiasing for Pre-trained Word Embeddings (P19-1)
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| Challenge: | Existing methods for debiasing word embeddings have shown discriminative biases . word embeds learnt from social media have shown to encode racist, offensive and discriminative language usage. |
| Approach: | They propose a method that preserves gender-related information while removing stereotypical gender biases from pre-trained word embeddings. |
| Outcome: | The proposed method preserves gender-related information while removing stereotypical discriminative gender biases from pre-trained word embeddings. |
Mitigating Gender Bias Amplification in Distribution by Posterior Regularization (2020.acl-main)
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| Challenge: | Recent studies show that data-driven machine learning models carry societal biases in the dataset they trained on. |
| Approach: | They propose to calibrate top predictions of a model by injecting corpus-level constraints to ensure that the gender disparity is not amplified. |
| Outcome: | The proposed method can almost remove bias amplification in the distribution with little loss of performance. |
OSCaR: Orthogonal Subspace Correction and Rectification of Biases in Word Embeddings (2021.emnlp-main)
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| Challenge: | Existing methods to mitigate stereotypical biases by linear projection are too aggressive . existing methods remove bias, but they also erase valuable information from word embeddings . |
| Approach: | They propose a bias-mitigating method that disentangles biased associations between concepts instead of removing concepts wholesale. |
| Outcome: | The proposed method disentangles biased associations between concepts rather than eliminating concepts wholesale. |