Black is to Criminal as Caucasian is to Police: Detecting and Removing Multiclass Bias in Word Embeddings (N19-1)
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| Challenge: | Existing methods to debias word embeddings in binary settings such as gender and religion are limited to binary labels, whereas word2vec embedders can be used to propagate biases. |
| Approach: | They propose a method to debias word embeddings in multiclass settings such as gender and religion, extending the work of Bolukbasi et al. (2016). |
| Outcome: | The proposed method maintains the efficacy in standard NLP tasks while maintaining the utility of embeddings. |
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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. |
| 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 . |
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. |
Double-Hard Debias: Tailoring Word Embeddings for Gender Bias Mitigation (2020.acl-main)
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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 . |
| Approach: | They propose a technique that purifies word embeddings against inferred gender subspaces . they propose to preserve distributional semantics of pre-trained word embeds while reducing gender bias . |
| Outcome: | The proposed technique preserves distributional semantics of pre-trained word embeddings while reducing gender bias to a larger degree than prior approaches. |
Unequal Representations: Analyzing Intersectional Biases in Word Embeddings Using Representational Similarity Analysis (2020.coling-main)
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| Challenge: | Specifically, we probe contextualized and non-contextualized word embeddings for evidence of intersectional biases against Black women. |
| Approach: | They propose a representational similarity analysis approach to detect human-like biases in word embeddings using representational similarities analysis. |
| Outcome: | The proposed approach aligns with intersectionality theory, which states that multiple identity categories layer on top of each other to create unique modes of discrimination that are not shared by any individual category. |
Measuring and Mitigating Racial Bias in Embedding Models: A Comparative Study for Law Enforcement Retrieval (2026.acl-industry)
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| Challenge: | Embedding models are often used for semantic retrieval in high-stakes domains such as law enforcement . racial descriptors affect similarity scores and retrieval rankings for semantically identical crime incidents . |
| Approach: | They propose to use racial descriptors to measure r&d bias in embedding models . they compute similarity scores between crime incidents and simple law enforcement queries . |
| Outcome: | The proposed methods show that racial descriptors affect similarity scores and retrieval rankings for semantically identical crime incidents. |
More than Minorities and Majorities: Understanding Multilateral Bias in Language Generation (2024.findings-acl)
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| Challenge: | Existing studies on bias dataset construction and mitigation focus on one demographic group . in real-world applications, there are more than two demographic groups at risk of the same bias. |
| Approach: | They propose to analyze and reduce biases across multiple demographic groups using a multi-demographic bias dataset. |
| Outcome: | The proposed method can mitigate biases among multiple demographic groups effectively, the authors show . |
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. |
Reducing Gender Bias in Abusive Language Detection (D18-1)
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| Challenge: | Abusive language detection models tend to be biased toward identity words of a certain group of people . recent studies have raised concerns about the robustness of such systems . |
| Approach: | They propose to use debiased word embeddings, gender swap data augmentation to reduce model bias . they also propose to fine-tune models with a larger corpus to correct such bias if needed . |
| Outcome: | The proposed methods reduce model bias by 90-98% and can be extended to correct model bias in other scenarios. |
Thesis Proposal: Auditing and Mitigating Demographic Bias in Multi-Stage Retrieval Systems for Criminal Justice Applications (2026.acl-srw)
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| Challenge: | racial descriptors alter embedding similarity scores and retrieval rankings, a new study shows . rife-specific biases can displace relevant records outside top-10 results, the study concludes . |
| Approach: | They propose to detect, measure, and mitigate racial bias in NLP systems deployed in criminal justice contexts . they propose to develop and evaluate debiasing techniques, validate synthetic findings on authentic law enforcement data . |
| Outcome: | The proposed research examines how bias propagates across retrieval pipelines . it shows that racial descriptors alter embedding similarity scores and retrieval rankings . |