On Gender Biases in Offensive Language Classification Models
We explore whether neural Natural Language Processing models trained to identify offensive language in tweets contain gender biases. We add historically gendered and gender ambiguous American names to an existing offensive language evaluation set to determine whether models? predictions are sensitive or robust to gendered names. While we see some evidence that these models might be prone to biased stereotypes that men use more offensive language than women, our results indicate that these models? binary predictions might not greatly change based upon gendered names.
Code (0)
등록된 구현이 없습니다.
Tasks
ClassificationMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Towards Equal Gender Representation in the Annotations of Toxic Language Detection
Classifiers tend to propagate biases present in the data on which they are trained. Hence, it is important to understand how the demographic identities of the annotators of comments affect the fairness of the resulting m…
FairnessTowards Safer AI Moderation: Evaluating LLM Moderators Through a Unified Benchmark Dataset and Advocating a Human-First Approach
As AI systems become more integrated into daily life, the need for safer and more reliable moderation has never been greater. Large Language Models (LLMs) have demonstrated remarkable capabilities, surpassing earlier mod…
Detecting Unintended Social Bias in Toxic Language Datasets
With the rise of online hate speech, automatic detection of Hate Speech, Offensive texts as a natural language processing task is getting popular. However, very little research has been done to detect unintended social b…
Should We Attend More or Less? Modulating Attention for Fairness
The advances in natural language processing (NLP) pose both opportunities and challenges. While recent progress enables the development of high-performing models for a variety of tasks, it also poses the risk of models l…
Fairnesstext-classificationText ClassificationDetecting Unintended Social Bias in Toxic Language Datasets
Hate speech and offensive texts are examples of damaging online content that target or promote hatred towards a group or individual member based on their actual or perceived features of identification, such as race, reli…