| Challenge: | Social biases are difficult to identify because human judgements in this domain can be unreliable. |
| Approach: | They propose an unsupervised approach to detecting implicit gender bias in text . their main challenge is forcing the model to focus on signs of implicit bias . |
| Outcome: | The proposed model reduces the influence of confounds by focusing on signs of implicit bias rather than other artifacts in the data. |
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Uncovering Implicit Gender Bias in Narratives through Commonsense Inference (2021.findings-emnlp)
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| Challenge: | Pre-trained language models learn harmful biases from their training corpora and may repeat these biase if used for generation. |
| Approach: | They focus on gender biases associated with the protagonist in model-generated stories and use a commonsense reasoning engine to uncover them. |
| Outcome: | The proposed model-generated stories are based on a commonsense reasoning engine and are able to uncover gender biases in the protagonist's motivations, attributes, mental states, and implications on others. |
Multi-Dimensional Gender Bias Classification (2020.emnlp-main)
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| Challenge: | a novel framework decomposes gender bias in text along several pragmatic and semantic dimensions . language is a primary means by which people communicate, express identities and categorize themselves . unwanted gender biases can affect downstream applications, leading to poor user experiences . |
| Approach: | They propose a framework that decomposes gender bias in text along several dimensions . they annotate eight large scale datasets with gender information and collect a benchmark . |
| Outcome: | The proposed framework decomposes gender bias in text along several pragmatic and semantic dimensions. |
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 . |
| Approach: | They propose to analyze gender bias based on four forms of representation bias and discuss the advantages and drawbacks of existing gender debiasing methods. |
| Outcome: | The proposed methods are based on four forms of representation bias and have advantages and drawbacks. |
Comparing Intrinsic Gender Bias Evaluation Measures without using Human Annotated Examples (2023.eacl-main)
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| Challenge: | Existing methods to evaluate gender biases in pre-trained language models have been limited by the cost and difficulties of recruiting human annotators. |
| Approach: | They propose a method to compare intrinsic gender bias evaluation measures without relying on human annotated examples. |
| Outcome: | The proposed method compares gender-based gender bias evaluation measures without human annotators without human input. |
Blind Men and the Elephant: Diverse Perspectives on Gender Stereotypes in Benchmark Datasets (2025.emnlp-main)
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| Challenge: | Existing benchmarks for measuring gender stereotypical bias in language models are inconsistencies . lack of explicit standards in data gathering can have detrimental effects on results . |
| Approach: | They propose that currently available benchmarks capture only partial facets of gender stereotypes . they apply a framework from social psychology to balance data across components of gender stereotypes based on stereotypical benchmarks. |
| Outcome: | The proposed framework improves correlation between different benchmarks by using simple balancing techniques. |
A Comparative Study of Explicit and Implicit Gender Biases in Large Language Models via Self-evaluation (2024.lrec-main)
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| Challenge: | Existing studies on the explicit and implicit biases in large language models (LLMs) focus on either explicit or implicit bias. |
| Approach: | They propose a self-evaluation-based two-stage measurement of explicit and implicit biases within large language models grounded in social psychology. |
| Outcome: | The proposed model is based on two stages of self-evaluation on state-of-the-art LLMs to measure explicit bias toward social targets, where bias is less likely to be self-recognized by the LLM. |
Unsupervised Discovery of Gendered Language through Latent-Variable Modeling (P19-1)
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| Challenge: | a recent study has focused on the ways in which language is gendered . positive adjectives used to describe women are more often related to their bodies . |
| Approach: | They propose a model that models adjective choice and its sentiment given the natural gender of a head noun. |
| Outcome: | The proposed model shows that positive adjectives used to describe women are more often related to their bodies than positive adjective words used to explain men. |
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. |
Pro-Woman, Anti-Man? Identifying Gender Bias in Stance Detection (2024.findings-acl)
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| Challenge: | Gender bias has been widely observed in NLP models, which can perpetuate harmful stereotypes and discrimination. |
| Approach: | They construct a dataset to measure gender bias in stance detection using 36k samples . they find that all models are gender-biased and prone to classify sentences that contain male nouns as Against and those with female noun as Favor . |
| Outcome: | The proposed dataset shows that all models are gender-biased and prone to classify sentences that contain male nouns as Against and those with female noun as Favor. |
How Gender Debiasing Affects Internal Model Representations, and Why It Matters (2022.naacl-main)
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| Challenge: | Existing studies of gender bias in NLP focus on extrinsic or intrinsic bias, but the relationship between extrindic and intrinsic bias is relatively unknown. |
| Approach: | They propose a framework to measure extrinsic and intrinsic bias together and propose metric to measure debiasing and intrinsic debiases. |
| Outcome: | The proposed framework provides a comprehensive perspective on bias in NLP models, which can be applied to deploy NLP systems in a more informed manner. |