Systematic Offensive Stereotyping (SOS) Bias in Language Models
In this paper, we propose a new metric to measure the SOS bias in language models (LMs). Then, we validate the SOS bias and investigate the effectiveness of removing it. Finally, we investigate the impact of the SOS bias in LMs on their performance and fairness on hate speech detection. Our results suggest that all the inspected LMs are SOS biased. And that the SOS bias is reflective of the online hate experienced by marginalized identities. The results indicate that using debias methods from the literature worsens the SOS bias in LMs for some sensitive attributes and improves it for others. Finally, Our results suggest that the SOS bias in the inspected LMs has an impact on their fairness of hate speech detection. However, there is no strong evidence that the SOS bias has an impact on the performance of hate speech detection.
Code (0)
등록된 구현이 없습니다.
Tasks
FairnessHate Speech DetectionSimilar Papers 제목 키워드 기반
SOS: Systematic Offensive Stereotyping Bias in Word Embeddings
Hate speech detection models aim to provide a safe environment for marginalised social groups to express themselves. However, the bias in these models could lead to silencing those groups. In this paper, we introduce the…
Hate Speech DetectionWord EmbeddingsSOS: Systematic Offensive Stereotyping Bias in Word Embeddings
Systematic Offensive stereotyping (SOS) in word embeddings could lead to associating marginalised groups with hate speech and profanity, which might lead to blocking and silencing those groups, especially on social media…
BlockingHate Speech DetectionWord EmbeddingsWait, am I Being Fair? Characterizing Deductive Stereotyping and Mitigating It with Fair-GCG
Warning: This paper contains several toxic and offensive statements. While reasoning generally improves fairness in recent large language models (LLMs), failures persist. In this work, we identify a failure mode, deducti…
Thesis Distillation: Investigating The Impact of Bias in NLP Models on Hate Speech Detection
This paper is a summary of the work done in my PhD thesis. Where I investigate the impact of bias in NLP models on the task of hate speech detection from three perspectives: explainability, offensive stereotyping bias, a…
FairnessHate Speech DetectionRedirected, Not Removed: Task-Dependent Stereotyping Reveals the Limits of LLM Alignments
How biased is a language model? The answer depends on how you ask. A model that refuses to choose between castes for a leadership role will, in a fill-in-the-blank task, reliably associate upper castes with purity and lo…