| 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. |
| Approach: | They propose a generic evaluation framework that detects unintended model biases related to named entities and requires no new annotations or corpora. |
| Outcome: | The proposed framework detects unintended model biases related to named entities and requires no new annotations or corpora. |
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| Challenge: | Recent studies show that NLP models are vulnerable to adversarial perturbations such as synonym substitutions or syntax-guided paraphrasing. |
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Predictive Biases in Natural Language Processing Models: A Conceptual Framework and Overview (2020.acl-main)
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| Challenge: | a growing number of studies address the effect of bias on predictions, but no unifying framework exists . a general phenomenon of biased predictive models in NLP is not recent, authors say . |
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BiasWipe: Mitigating Unintended Bias in Text Classifiers through Model Interpretability (2024.emnlp-main)
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| Challenge: | Existing methods for detecting biases are biased because of confounding variables . authors propose a method to detect the biased classifier on any type of unlabeled data . |
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Perturbed examples reveal invariances shared by language models (2024.findings-acl)
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| Challenge: | Rapid growth in natural language processing (NLP) research has led to numerous new models outpacing our understanding of how they compare to established ones. |
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On Measures of Biases and Harms in NLP (2022.findings-aacl)
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Sunipa Dev, Emily Sheng, Jieyu Zhao, Aubrie Amstutz, Jiao Sun, Yu Hou, Mattie Sanseverino, Jiin Kim, Akihiro Nishi, Nanyun Peng, Kai-Wei Chang
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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: | Existing large language models struggle with systematically perturbed data designed to evade detection mechanisms. |
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It’s Morphin’ Time! Combating Linguistic Discrimination with Inflectional Perturbations (2020.acl-main)
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| Challenge: | Existing work on societal bias in NLP focuses on race and gender . linguistic background is a unique attribute that has been largely ignored in the field . |
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