| 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. |
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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. |
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. |
Adversarial Scrubbing of Demographic Information for Text Classification (2021.emnlp-main)
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Somnath Basu Roy Chowdhury, Sayan Ghosh, Yiyuan Li, Junier Oliva, Shashank Srivastava, Snigdha Chaturvedi
| Challenge: | Existing frameworks to debias contextual representations can encode undesirable attributes, like demographic associations of the users, while being trained for an unrelated task. |
| Approach: | They propose an adversarial learning framework to debias contextual representations by encoding undesirable attributes while being trained for an unrelated task. |
| Outcome: | The proposed framework debiases representations on 8 datasets while remaining informative on the target task. |
BLIND: Bias Removal With No Demographics (2023.acl-long)
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| 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. |
Avoiding the Hypothesis-Only Bias in Natural Language Inference via Ensemble Adversarial Training (2020.emnlp-main)
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| Challenge: | Neural models pick up on annotation artefacts and spurious correlations, resulting in learning sentences that suffer from the same biases. |
| Approach: | They propose to tackle this problem by using adversarial training to reduce the bias in sentence representations by using an ensemble of adversaries. |
| Outcome: | The proposed approach produces more robust models outperforming previous de-biasing efforts when generalised to 12 other NLI datasets. |
The Authors Matter: Understanding and Mitigating Implicit Bias in Deep Text Classification (2021.findings-acl)
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| Challenge: | Existing studies on text classification have focused on the bias towards the individuals mentioned in the text content. |
| Approach: | They propose a framework to mitigate implicit bias in text classification models based on demographic attributes of authors . they propose to use this framework to train deep text classifiers to make predictions on the right features . |
| Outcome: | The proposed framework outperforms existing models significantly in fairness and performance. |
Adversarial Decomposition of Text Representation (N19-1)
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| Challenge: | a new method for adversarial decomposition of text representations is proposed . it is capable of fine-grained controlled change of different aspects of the input sentence . |
| Approach: | They propose a method for adversarial decomposition of text representation . they use vectors responsible for a specific aspect of the input sentence . |
| Outcome: | The proposed method outperforms the embeddings of a regular autoencoder on paraphrase detection tasks. |
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. |
Counterfactual Inference for Text Classification Debiasing (2021.acl-long)
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| Challenge: | Existing methods to capture unintended dataset biases are expensive and require elaborate balancing strategies. |
| Approach: | They propose a model-agnostic text classification debiasing framework which can effectively avoid employing data manipulations or designing balancing mechanisms. |
| Outcome: | The proposed framework can effectively avoid data manipulations or designing balancing mechanisms to capture unintended dataset biases. |
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. |