Centering the Margins: Outlier-Based Identification of Harmed Populations in Toxicity Detection (2023.emnlp-main)
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| Challenge: | toxicity detection models focus on marginalized groups, but they obscure harms faced by intersectional subgroups. |
| Approach: | They use outlier detection to identify text about people with demographic attributes distant from the "norm" they find model performance is worse for demographic outliers than non-outliers . |
| Outcome: | The proposed model performance is worse for outliers than non-outliers, the authors say . their analysis also shows that outlier analysis can identify harms faced by intersectional groups . |
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| Challenge: | Existing datasets focused on gender or racial biases are not designed for the gaming industry, a concern for models built for toxicity detection in videogames’ written chat. |
| Approach: | They propose to use reactivity analysis to highlight oversensitive terms using a language model developed by Ubisoft for toxicity detection on videogame’s written chat and Perspective API to generate a list of terms that trigger the models to varying degrees. |
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On the Role of Speech Data in Reducing Toxicity Detection Bias (2025.naacl-long)
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Samuel Bell, Mariano Coria Meglioli, Megan Richards, Eduardo Sánchez, Christophe Ropers, Skyler Wang, Adina Williams, Levent Sagun, Marta R. Costa-jussà
| Challenge: | Text toxicity detection systems produce disproportionate rates of false positives on demographic groups . toxicity classification systems often misinterpret benign group mentions as toxic . |
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Detoxifying Language Models Risks Marginalizing Minority Voices (2021.naacl-main)
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| Challenge: | Existing detoxification techniques have been proposed to mitigate toxic LM generations . e.g., detoxification makes LMs more brittle to distribution shift, especially on language used by marginalized groups . |
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ModelCitizens: Representing Community Voices in Online Safety (2025.emnlp-main)
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Ashima Suvarna, Christina A Chance, Karolina Naranjo, Hamid Palangi, Sophie Hao, Thomas Hartvigsen, Saadia Gabriel
| Challenge: | Existing toxic language detection models are trained on annotations that collapse diverse perspectives into a single ground truth. |
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Detecting Community Sensitive Norm Violations in Online Conversations (2021.findings-emnlp)
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Chan Young Park, Julia Mendelsohn, Karthik Radhakrishnan, Kinjal Jain, Tushar Kanakagiri, David Jurgens, Yulia Tsvetkov
| Challenge: | Existing efforts to identify unacceptable behavior have focused on toxicity as the sole form of community norm violation. |
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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. |
| 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. |
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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. |
| Approach: | They propose to debiase toxic language detection models using lexical and dialectal markers using synthetic labels instead of traditional methods. |
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ROBBIE: Robust Bias Evaluation of Large Generative Language Models (2023.emnlp-main)
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David Esiobu, Xiaoqing Tan, Saghar Hosseini, Megan Ung, Yuchen Zhang, Jude Fernandes, Jane Dwivedi-Yu, Eleonora Presani, Adina Williams, Eric Smith
| Challenge: | generative large language models (LLMs) are becoming more performant and prevalent . we need tools to measure and improve their fairness, authors say . |
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Social Bias Probing: Fairness Benchmarking for Language Models (2024.emnlp-main)
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| Challenge: | Existing methods for evaluating social biases in language models have been limited to binary association tests on small datasets. |
| Approach: | They propose a framework for probing language models for social biases by assessing disparate treatment . they use a large-scale benchmark to examine the diversity of identities and stereotypes . |
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LOGAN: Local Group Bias Detection by Clustering (2020.emnlp-main)
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| Challenge: | a number of machine learning models inherit and amplify the societal biases in data. |
| Approach: | a new bias detection technique based on clustering is proposed to detect local biases in data . authors propose to use LOGAN to analyze local bias in data. |
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