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A Study on Bias Detection and Classification in Natural Language Processing

arXiv 2408.07479 ↗First posted 14 Aug 2024cs.CL cs.AIcited by 0

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Abstract (arXiv)

Human biases have been shown to influence the performance of models and algorithms in various fields, including Natural Language Processing. While the study of this phenomenon is garnering focus in recent years, the available resources are still relatively scarce, often focusing on different forms or manifestations of biases. The aim of our work is twofold: 1) gather publicly-available datasets and determine how to better combine them to effectively train models in the task of hate speech detection and classification; 2) analyse the main issues with these datasets, such as scarcity, skewed resources, and reliance on non-persistent data. We discuss these issues in tandem with the development of our experiments, in which we show that the combinations of different datasets greatly impact the models' performance.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 25 of its 45 references that are in the map. First posted on arXiv 14 Aug 2024.

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25 of 45 references.

References · 25

  1. 2016Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddingscited by 1,304
  2. 2017Automated Hate Speech Detection and the Problem of Offensive Languagecited by 724
  3. 2020CrowS-Pairs: A Challenge Dataset for Measuring Social Biases in Masked Language Modelscited by 703
  4. 2018Mitigating Unwanted Biases with Adversarial Learningcited by 663
  5. 2018Gender Bias in Coreference Resolution: Evaluation and Debiasing Methodscited by 657
  6. 2018Large Scale Crowdsourcing and Characterization of Twitter Abusive Behaviorcited by 265
  7. 2018Examining Gender and Race Bias in Two Hundred Sentiment Analysis Systemscited by 234
  8. 2020Learning from the Worst: Dynamically Generated Datasets to Improve Online Hate Detectioncited by 217
  9. 2017Deep Learning for Hate Speech Detection in Tweetscited by 180
  10. 2020Detecting Emergent Intersectional Biases: Contextualized Word Embeddings Contain a Distribution of Human-like Biasescited by 167
  11. 2023A Categorical Archive of ChatGPT Failurescited by 167
  12. 2019Queens are Powerful too: Mitigating Gender Bias in Dialogue Generationcited by 152
13 more
  1. 2021RedditBias: A Real-World Resource for Bias Evaluation and Debiasing of Conversational Language Modelscited by 149
  2. 2019Assessing Social and Intersectional Biases in Contextualized Word Representationscited by 134
  3. 2019Multilingual and Multi-Aspect Hate Speech Analysiscited by 133
  4. 2019CONAN -- COunter NArratives through Nichesourcing: a Multilingual Dataset of Responses to Fight Online Hate Speechcited by 109
  5. 2019Evaluating the Underlying Gender Bias in Contextualized Word Embeddingscited by 94
  6. 2021What's in the Box? A Preliminary Analysis of Undesirable Content in the Common Crawl Corpuscited by 79
  7. 2021A Survey of Race, Racism, and Anti-Racism in NLPcited by 74
  8. 2019Gender-preserving Debiasing for Pre-trained Word Embeddingscited by 61
  9. 2020Mitigating Gender Bias for Neural Dialogue Generation with Adversarial Learningcited by 44
  10. 2019Examining Gender Bias in Languages with Grammatical Gendercited by 40
  11. 2019Multi-label Categorization of Accounts of Sexism using a Neural Frameworkcited by 19
  12. 2020Multilingual Contextual Affective Analysis of LGBT People Portrayals in Wikipediacited by 9
  13. 2021Towards Equal Gender Representation in the Annotations of Toxic Language Detectioncited by 9

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