BiasWipe: Mitigating Unintended Bias in Text Classifiers through Model Interpretability (2024.emnlp-main)
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| Challenge: | Existing methods to mitigate unintended bias in social media platforms are re-training and adding extra parameters to the model. |
| Approach: | They propose a technique to mitigate unintended bias in language models by pruning the neuron weights responsible for univ bias. |
| Outcome: | The proposed technique achieves fairness by pruning the neuron weights responsible for unintended bias without loss of original performance. |
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| Challenge: | Recent studies show that NLP models trained on standard English produce biased outcomes against underrepresented English varieties. |
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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. |
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Challenges in Automated Debiasing for Toxic Language Detection (2021.eacl-main)
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| Challenge: | Existing methods for debiasing toxic language data are limited in their ability to prevent biased behavior in toxic language detection systems. |
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Perturbation Sensitivity Analysis to Detect Unintended Model Biases (D19-1)
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| Challenge: | Recent research shows that data-driven NLP models may inadvertently capture, reflect and sometimes amplify various social biases present in the language data they are trained on. |
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When and Why Does Bias Mitigation Work? (2023.findings-emnlp)
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| Challenge: | Neural models exploit shallow surface features to perform language understanding tasks, rather than learning the deeper language understanding and reasoning skills that practitioners desire. |
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Debiasing Masks: A New Framework for Shortcut Mitigation in NLU (2022.emnlp-main)
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| Challenge: | Debiasing language models from unwanted behaviors in natural language understanding datasets is a topic with increasing interest in the NLP community. |
| Approach: | They propose a method to debiase language models from unwanted behaviors in NLU tasks by identifying pruning masks that can be applied to a finetuned model. |
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Explaining Toxic Text via Knowledge Enhanced Text Generation (2022.naacl-main)
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| Challenge: | Existing work on toxic speech classification relies on generic and repetitive explanations . elucidating toxic speech can help with downstream tasks such as debiasing . |
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Self-Diagnosis and Self-Debiasing: A Proposal for Reducing Corpus-Based Bias in NLP (2021.tacl-1)
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| Challenge: | Pretrained language models pick up and reproduce undesirable biases when trained on large, unfiltered crawls from the Internet. |
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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 . |
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Controlling Bias Exposure for Fair Interpretable Predictions (2022.findings-emnlp)
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| Challenge: | Existing approaches to reduce bias in NLP tasks focus on protecting or isolating information related to a sensitive attribute, but they lack control over how much bias is required to be removed. |
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