| Challenge: | Existing studies on class imbalance and mitigating bias have focused on the latter . a skewed class distribution hurts the performance of deep learning models, and is often referred to as "stereotyping" |
| Approach: | They propose to extend a margin-loss based approach to enforce fairness by using tweet sentiment and occupation classification to mitigate class imbalance and demographic bias. |
| Outcome: | The proposed methods help mitigate class imbalance and demographic biases through controlled experiments. |
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A Survey of Methods for Addressing Class Imbalance in Deep-Learning Based Natural Language Processing (2023.eacl-main)
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| Challenge: | Developing methods to improve model performance in imbalanced data settings has been an active area for decades . |
| Approach: | They propose to use sampling, data augmentation, choice of loss function, staged learning, or model design to address class imbalance in NLP. |
| Outcome: | The proposed approaches are evaluated on a variety of NLP tasks or in the computer vision community. |
Balancing out Bias: Achieving Fairness Through Balanced Training (2022.emnlp-main)
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| Challenge: | Existing approaches to reducing group bias do not account for correlations between author demographics and linguistic variables, limiting their effectiveness. |
| Approach: | They extend a method for countering group bias using balanced training by balancing each demographic group in training and using protected attributes as input. |
| Outcome: | The proposed model outperforms all other methods when combined with balanced training. |
Systematic Evaluation of Predictive Fairness (2022.aacl-main)
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| Challenge: | Several methods have been proposed to mitigate bias in training on biased datasets. |
| Approach: | They propose to examine the effect of target class imbalance and stereotyping on model performance by analyzing binary classification, profession prediction and regression tasks. |
| Outcome: | The proposed methods show that data conditions have a strong influence on relative model performance. |
Bias and Fairness in Natural Language Processing (D19-2)
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| Challenge: | a tutorial will review the history of bias and fairness studies in machine learning and language processing . |
| Approach: | This tutorial reviews the history of bias and fairness studies in machine learning and language processing . it presents recent community effort to quantify and mitigat bias in natural language processing models . |
| Outcome: | This tutorial reviews the history of bias and fairness studies in machine learning and language processing . it aims to quantify and mitigate bias in natural language processing models for a wide spectrum of tasks . |
Balancing Methods for Multi-label Text Classification with Long-Tailed Class Distribution (2021.emnlp-main)
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| Challenge: | Multi-label text classification is a challenging task because it requires capturing label dependencies. |
| Approach: | They propose to use distribution-balanced loss functions to solve label dependency problems in multi-label text classification by capturing label dependencies from a fixed-set of labels. |
| Outcome: | The proposed loss function addresses both the class imbalance and label linkage problems and outperforms other loss functions. |
Debiasing Text Safety Classifiers through a Fairness-Aware Ensemble (2024.emnlp-industry)
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| Challenge: | Increasing use of large language models (LLMs) require performant guardrails to ensure the safety of inputs and outputs . when these guardrail are trained on imbalanced data, they can learn the societal biases resulting from the model's performance. |
| Approach: | They propose a method for mitigating counterfactual fairness in closed-source text safety classifiers by using a debiasing regularizer and a threshold-agnostic metric. |
| Outcome: | The proposed method outperforms classifiers and acts as a debiasing regularizer . it uses threshold-agnostic metrics and Fair Data Reweighting (FDW) to assess the counterfactual fairness of a model . |
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 . |
| Approach: | They propose to use different intrinsic bias mitigation strategies to mitigate gender bias in contextualized language models. |
| Outcome: | The proposed probe shows that some mitigation techniques can hide gender bias . the probe also shows that not all mitigation techniques fool extrinsic bias despite their use . |
Contrastive Learning as a Polarizer: Mitigating Gender Bias by Fair and Biased sentences (2024.findings-naacl)
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| Challenge: | Recent studies have highlighted social biases inherent in training data can lead models to learn and propagate them. |
| Approach: | They propose a contrastive learning method that uses anchor points to push further negatives and pull closer positives within the representation space. |
| Outcome: | The proposed method achieves state-of-the-art in the ICAT score on the StereoSet, a benchmark for measuring bias in models. |
Instance-Selection-Inspired Undersampling Strategies for Bias Reduction in Small and Large Language Models for Binary Text Classification (2025.acl-long)
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Guilherme Fonseca, Washington Cunha, Gabriel Prenassi, Marcos André Gonçalves, Leonardo Chaves Dutra Da Rocha
| Challenge: | Existing methods to mitigate class imbalanced datasets are limited by existing methods. |
| Approach: | They propose two undersampling methods inspired by state-of-the-art Instance Selection techniques to mitigate class imbalance bias in ATC. |
| Outcome: | The proposed methods reduce classifier bias (56%) across all datasets without effectiveness loss while improving efficiency (1.6x speedup), scalability and reducing carbon emissions (up to 50%). |
Fair Enough: Standardizing Evaluation and Model Selection for Fairness Research in NLP (2023.eacl-main)
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| Challenge: | Modern NLP systems exhibit a range of biases, which a growing literature on model debiasing attempts to correct. |
| Approach: | They propose to clarify the current situation and plot a course for meaningful progress in fair learning by making clear inter-relations among the current gamut of methods and their relation to fairness theory. |
| Outcome: | The proposed approach addresses the practical problem of model selection, which involves a trade-off between fairness and accuracy and has led to systemic issues in fairness research. |