| 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. |
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Towards Automatic Bias Detection in Knowledge Graphs (2021.findings-emnlp)
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| Challenge: | Recent studies have shown that knowledge graphs are prone to various social biases, and have proposed multiple methods for debiasing them. |
| Approach: | They propose a framework for identifying biases present in knowledge graph embeddings based on numerical bias metrics. |
| Outcome: | The proposed framework can be extended to further bias definitions and applications. |
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 . |
Understanding Gender Bias in Knowledge Base Embeddings (2022.acl-long)
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| 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. |
From Prejudice to Parity: A New Approach to Debiasing Large Language Model Word Embeddings (2025.coling-main)
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| Challenge: | Existing work in this field has looked most commonly into gender bias, racial bias, and religious bias. |
| Approach: | They propose an algorithm that uses a neural network to perform ‘soft debiasing’ and build on the seminal work of (CITATION) and (CitATION). |
| Outcome: | The proposed algorithm outperforms current methods on gender, race, and religion metrics on a wide range of metrics. |
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. |
Unlabeled Debiasing in Downstream Tasks via Class-wise Low Variance Regularization (2024.emnlp-main)
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| Challenge: | Existing methods for debiasing depend on attribute labels and target attributes. |
| Approach: | They propose a method that uses class-wise variance of embeddings to reduce the effects of debiasing on a downstream task. |
| Outcome: | The proposed method outperforms baselines that rely on attribute labels while maintaining performance on the target task. |
Learning Bias-reduced Word Embeddings Using Dictionary Definitions (2022.findings-acl)
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| Challenge: | Existing word embeddings have undesirable gender, racial, and religious biases . DD-GloVe is a train-time debiasing algorithm that uses dictionary definitions based on word definitions. |
| Approach: | They propose a dictionary-guided loss function that encourages word embeddings to be similar to their relatively neutral dictionary definition representations. |
| Outcome: | The proposed algorithm can learn word embeddings by leveraging dictionary definitions. |
The Medium Is Not the Message: Deconfounding Document Embeddings via Linear Concept Erasure (2025.emnlp-main)
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| Challenge: | Embedding-based similarity metrics can be influenced by content dimensions and spurious attributes like the text’s source or language. |
| Approach: | They propose a debiasing algorithm that removes observed confounders from encoder representations and removes them from the encoder. |
| Outcome: | The proposed method improves on out-of-distribution benchmarks and on benchmarks, but performance is not affected. |
Marked Attribute Bias in Natural Language Inference (2021.findings-acl)
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| Challenge: | Existing tests for gender-biased word embeddings do not address marked attribute bias . authors propose a new type of intrinsic bias measure for static word embeds . |
| Approach: | They propose a method to detect gender-biased word embeddings in a downstream NLP application . they propose 'debiasing' method to measure the marked attribute bias in embeddable word embeds . |
| Outcome: | The proposed method achieves best results on the marked attribute bias test set. |
Dictionary-based Debiasing of Pre-trained Word Embeddings (2021.eacl-main)
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| Challenge: | Existing methods for learning word embeddings using dictionaries do not require access to training resources or knowledge regarding the word embeds used. |
| Approach: | They propose a method for debiasing pre-trained word embeddings using dictionaries . they learn constraints that must be satisfied by unbiased word embeds from dictionary definitions . |
| Outcome: | The proposed method removes unfair biases encoded in pre-trained word embeddings while preserving useful semantics. |