MisgenderMender: A Community-Informed Approach to Interventions for Misgendering (2024.naacl-long)
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| Challenge: | Misgendering is the act of incorrectly addressing someone’s gender and is pervasive in everyday use platforms and technologies. |
| Approach: | They propose a task and evaluation dataset to assess the effectiveness of automated misgendering interventions for text-based misgending in the US. |
| Outcome: | The proposed dataset includes 3790 instances of social media content and LLM-generations about non-cisgender public figures, annotated for the presence of misgendering, with additional annotations for correcting misgending in LLM generated text. |
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| Challenge: | Misgendering is the act of referring to someone by using words that do not match their chosen identity. |
| Approach: | They propose to use a participatory-design approach to assess and mitigate misgendering across 42 languages and dialects using a human-in-the-loop approach. |
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MISGENDERED: Limits of Large Language Models in Understanding Pronouns (2023.acl-long)
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| Challenge: | excluding non-binary gender identities can perpetuate harm against non-bisexual individuals through exclusion and marginalization. |
| Approach: | They propose a framework for evaluating large language models’ ability to correctly use preferred pronouns. |
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Stereotypes and Smut: The (Mis)representation of Non-cisgender Identities by Text-to-Image Models (2023.findings-acl)
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| Challenge: | Initial studies have pointed to the potential for harm due to predictive bias, reflecting and potentially reinforcing cultural stereotypes. |
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RtGender: A Corpus for Studying Differential Responses to Gender (L18-1)
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| Challenge: | Prior work on linguistic gender difference and communications about gender has focused on language about or portraying persons of a particular gender. |
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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 . |
| Approach: | They propose a knowledge-informed encoder-decoder framework to generate toxic text explanations . they use multiple knowledge sources to generate detailed explanations of toxic text . |
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Gender Identity in Pretrained Language Models: An Inclusive Approach to Data Creation and Probing (2024.findings-emnlp)
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| Challenge: | Pretrained language models encode binary gender information of text authors, raising the risk of skewed representations and downstream harms. |
| Approach: | They use a corpus of YouTube transcripts from transgender, cisgender and non-binary speakers to examine whether pretrained language models encode binary gender information. |
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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. |
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MisinfoEval: Generative AI in the Era of “Alternative Facts” (2024.emnlp-main)
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| Challenge: | Existing efforts to address misinformation on social media platforms are hampered by user biases and scalability challenges. |
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Annotating Online Misogyny (2021.acl-long)
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| Challenge: | Online misogyny is a category of online abusive language with serious and harmful social consequences. |
| Approach: | They propose an iterative annotation process and a taxonomy of labels for annotating misogyny in natural written language and cite a high-quality dataset of annotated posts from social media posts. |
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A Just and Comprehensive Strategy for Using NLP to Address Online Abuse (P19-1)
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| Challenge: | Current methods to detect online abuse focus on a narrow definition of abuse to detriment of victims seeking validation and solutions. |
| Approach: | They argue that the NLP community needs to make three substantive changes to tackle both more subtle and more serious forms of abuse. |
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