Mitigating Bias in Session-based Cyberbullying Detection: A Non-Compromising Approach (2021.acl-long)
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| Challenge: | Existing efforts to enhance the performance of session-based cyberbullying detection have overlooked unintended social biases in existing datasets. |
| Approach: | They propose a model-agnostic debiasing strategy that leverages a reinforcement learning technique to mitigate unintended biases in existing datasets. |
| Outcome: | The proposed approach can mitigate unintended biases without impairing the detection performance. |
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HENIN: Learning Heterogeneous Neural Interaction Networks for Explainable Cyberbullying Detection on Social Media (2020.emnlp-main)
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Kanishk Verma, Kolawole John Adebayo, Joachim Wagner, Megan Reynolds, Rebecca Umbach, Tijana Milosevic, Brian Davis
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| Challenge: | Existing methods for detection of biases in contextual language models are inconsistent and inconclusive. |
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Cyberbullying Classifiers are Sensitive to Model-Agnostic Perturbations (2022.lrec-1)
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| Challenge: | toxicity classifiers rely on lexical cues, so creative language use can be detrimental to utility of current corpora and state-of-the-art models. |
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| Challenge: | Existing methods for debiasing large language models incur high human and computational costs and are limited in their effectiveness. |
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Kanishk Verma, Sri Balaaji, Joachim Wagner, Arefeh Kazemi, Darragh Mccashin, Isobel Walsh@dcu, Sayani Basak, Sinan Asci, Yelena Cherkasova, Alexandros Poulis, James Ohiggins Norman, Rebecca Umbach Umbach, Tijana Milosevic, Brian Davis
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Open-DeBias: Toward Mitigating Open-Set Bias in Language Models (2025.findings-emnlp)
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| Challenge: | Existing approaches to addressing harmful biases in LLMs are limited to predefined categories . a novel, data-efficient, and parameter-efficient debiasing method is proposed to mitigate existing social and stereotypical biase . |
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