| Challenge: | Numerous methods to mitigate social biases require prior knowledge of the demographics in the dataset, such as gender or race. |
| Approach: | They propose a method for bias removal without prior knowledge of demographics in the dataset. |
| Outcome: | Experiments with racial and gender biases in sentiment classification and occupation classification tasks show that BLIND mitigates biase . BLINT is competitive with methods that require demographic information and sometimes surpasses them. |
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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 . |
Adversarial Removal of Demographic Attributes from Text Data (D18-1)
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| Challenge: | Recent advances in Representation Learning and Adversarial Training remove unwanted features from the learned representation. |
| Approach: | They show that demographic information of authors is encoded in the intermediate representations learned by text-based neural classifiers. |
| Outcome: | The proposed approach achieves higher accuracies on the same dataset, the authors show . they show that the proposed approach is effective in removing unwanted features from the learned representations. |
Adversarial Removal of Demographic Attributes Revisited (D19-1)
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| Challenge: | Several approaches have been proposed to learn classifiers that are invariant (unbiased with respect) to protected attributes. |
| Approach: | They propose to use a diagnostic classifier trained on a held-out subsample to find protected attributes for mention detection at above-chance levels. |
| Outcome: | The proposed classifier generalizes poorly to new in-domain and new domains, suggesting it relies on correlations specific to their particular data sample. |
Leveraging Prototypical Representations for Mitigating Social Bias without Demographic Information (2024.naacl-short)
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| Challenge: | Existing approaches to mitigate social biases require explicit annotation of demographic information for each sample. |
| Approach: | They propose a method that leverages predefined demographic texts and incorporates a regularization term during the fine-tuning process to mitigate bias in language models. |
| Outcome: | The proposed method outperforms debiasing methods with limited demographic-annotated data. |
Don’t Just Clean It, Proxy Clean It: Mitigating Bias by Proxy in Pre-Trained Models (2022.findings-emnlp)
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| Challenge: | Transformer-based pre-trained models can encode societal biases in their contextual representations and in downstream predictions when fine-tuned on task-specific data. |
| Approach: | They propose an approach that selectively eliminates stereotypical associations at fine-tuning, so that the model doesn't learn to excessively rely on those signals. |
| Outcome: | The proposed approach reduces biases from identity words and frequently co-occurring proxies by > 60% in toxicity classification, and also extends to multiple identities. |
Towards Debiasing NLU Models from Unknown Biases (2020.emnlp-main)
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| Challenge: | Recent proposed debiasing methods rely on the assumption that the types of bias should be known a-priori, which limits their application to many NLU tasks and datasets. |
| Approach: | They propose a framework that prevents models from mainly utilizing biases without knowing them in advance. |
| Outcome: | The proposed framework allows existing methods to retain performance improvement on challenge datasets without specifically targeting biases. |
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. |
Demographics Should Not Be the Reason of Toxicity: Mitigating Discrimination in Text Classifications with Instance Weighting (2020.acl-main)
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| Challenge: | Recent research has found that text classification datasets contain certain unintended biases, such as text containing demographic identity-terms that are more likely to be abusive. |
| Approach: | They propose a model-agnostic debiasing framework that recovers the non-discrimination distribution using instance weighting, which does not require extra resources or annotations apart from a pre-defined set of demographic identity-terms. |
| Outcome: | The proposed framework alleviates the unintended biases without hurting models’ generalization ability. |
Fighting Bias With Bias: Promoting Model Robustness by Amplifying Dataset Biases (2023.findings-acl)
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| Challenge: | Recent work sought to develop robust, unbiased models by filtering biased examples from training sets. |
| Approach: | They propose to filter out biased examples from training sets to improve models' performance. |
| Outcome: | The proposed evaluation framework is more challenging than the original dataset splits and even more challenging that hand-crafted challenge sets. |
Privacy Preserving Data Selection for Bias Mitigation in Speech Models (2025.acl-industry)
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Alkis Koudounas, Eliana Pastor, Vittorio Mazzia, Manuel Giollo, Thomas Gueudre, Elisa Reale, Luca Cagliero, Sandro Cumani, Luca De Alfaro, Elena Baralis, Daniele Amberti
| Challenge: | Existing methods for identifying subgroups raise privacy concerns and gather sensitive information at runtime might be impractical. |
| Approach: | They propose a method to identify and train underperforming subgroups and train a model to predict if an utterance belongs to these subgroup. |
| Outcome: | The proposed method reduces biases and improves performance on intent classification and automatic speech recognition tasks. |