Analyzing Hate Speech Data along Racial, Gender and Intersectional Axes

• URL: http://arxiv.org/abs/2205.06621v1
• Date: Fri, 13 May 2022 13:13:46 GMT
• Title: Analyzing Hate Speech Data along Racial, Gender and Intersectional Axes
• Authors: Antonis Maronikolakis, Philip Baader, Hinrich Sch\"utze
• Abstract summary: We investigate bias in hate speech datasets along racial, gender and intersectional axes. We identify strong bias against African American English (AAE), masculine and AAE+Masculine tweets.
• Score: 1.5039745292757671
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: To tackle the rising phenomenon of hate speech, efforts have been made towards data curation and analysis. When it comes to analysis of bias, previous work has focused predominantly on race. In our work, we further investigate bias in hate speech datasets along racial, gender and intersectional axes. We identify strong bias against African American English (AAE), masculine and AAE+Masculine tweets, which are annotated as disproportionately more hateful and offensive than from other demographics. We provide evidence that BERT-based models propagate this bias and show that balancing the training data for these protected attributes can lead to fairer models with regards to gender, but not race.

Related papers

• What Do Llamas Really Think? Revealing Preference Biases in Language Model Representations [62.91799637259657]
  Do large language models (LLMs) exhibit sociodemographic biases, even when they decline to respond? We study this research question by probing contextualized embeddings and exploring whether this bias is encoded in its latent representations. We propose a logistic Bradley-Terry probe which predicts word pair preferences of LLMs from the words' hidden vectors.
  arXiv  Detail & Related papers  (2023-11-30T18:53:13Z)
• Will the Prince Get True Love's Kiss? On the Model Sensitivity to Gender Perturbation over Fairytale Texts [87.62403265382734]
  Recent studies show that traditional fairytales are rife with harmful gender biases. This work aims to assess learned biases of language models by evaluating their robustness against gender perturbations.
  arXiv  Detail & Related papers  (2023-10-16T22:25:09Z)
• The Impact of Debiasing on the Performance of Language Models in Downstream Tasks is Underestimated [70.23064111640132]
  We compare the impact of debiasing on performance across multiple downstream tasks using a wide-range of benchmark datasets. Experiments show that the effects of debiasing are consistently emphunderestimated across all tasks.
  arXiv  Detail & Related papers  (2023-09-16T20:25:34Z)
• Fairness in AI Systems: Mitigating gender bias from language-vision models [0.913755431537592]
  We study the extent of the impact of gender bias in existing datasets. We propose a methodology to mitigate its impact in caption based language vision models.
  arXiv  Detail & Related papers  (2023-05-03T04:33:44Z)
• Uncovering Implicit Gender Bias in Narratives through Commonsense Inference [21.18458377708873]
  We study gender biases associated with the protagonist in model-generated stories. We focus on implicit biases, and use a commonsense reasoning engine to uncover them.
  arXiv  Detail & Related papers  (2021-09-14T04:57:45Z)
• How True is GPT-2? An Empirical Analysis of Intersectional Occupational Biases [50.591267188664666]
  Downstream applications are at risk of inheriting biases contained in natural language models. We analyze the occupational biases of a popular generative language model, GPT-2. For a given job, GPT-2 reflects the societal skew of gender and ethnicity in the US, and in some cases, pulls the distribution towards gender parity.
  arXiv  Detail & Related papers  (2021-02-08T11:10:27Z)
• Argument from Old Man's View: Assessing Social Bias in Argumentation [20.65183968971417]
  Social bias in language poses a problem with ethical impact for many NLP applications. Recent research has shown that machine learning models trained on respective data may not only adopt, but even amplify the bias. We study the existence of social biases in large English debate portals.
  arXiv  Detail & Related papers  (2020-11-24T10:39:44Z)
• Hate Speech Detection and Racial Bias Mitigation in Social Media based on BERT model [1.9336815376402716]
  We introduce a transfer learning approach for hate speech detection based on an existing pre-trained language model called BERT. We evaluate the proposed model on two publicly available datasets annotated for racism, sexism, hate or offensive content on Twitter.
  arXiv  Detail & Related papers  (2020-08-14T16:47:25Z)
• Intersectional Bias in Hate Speech and Abusive Language Datasets [0.3149883354098941]
  African American tweets were up to 3.7 times more likely to be labeled as abusive. African American male tweets were up to 77% more likely to be labeled as hateful. This study provides the first systematic evidence on intersectional bias in datasets of hate speech and abusive language.
  arXiv  Detail & Related papers  (2020-05-12T16:58:48Z)
• Multi-Dimensional Gender Bias Classification [67.65551687580552]
  Machine learning models can inadvertently learn socially undesirable patterns when training on gender biased text. We propose a general framework that decomposes gender bias in text along several pragmatic and semantic dimensions. Using this fine-grained framework, we automatically annotate eight large scale datasets with gender information.
  arXiv  Detail & Related papers  (2020-05-01T21:23:20Z)
• Towards Controllable Biases in Language Generation [87.89632038677912]
  We develop a method to induce societal biases in generated text when input prompts contain mentions of specific demographic groups. We analyze two scenarios: 1) inducing negative biases for one demographic and positive biases for another demographic, and 2) equalizing biases between demographics.
  arXiv  Detail & Related papers  (2020-05-01T08:25:11Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.