Human and LLM Biases in Hate Speech Annotations: A Socio-Demographic Analysis of Annotators and Targets

TL;DR

This paper investigates socio-demographic biases in human and LLM hate speech annotations, revealing significant differences and providing insights for improving AI-based detection systems.

Contribution

It introduces a comprehensive socio-demographic analysis of biases in hate speech annotations from both humans and LLMs, filling a gap in understanding bias sources.

Findings

Widespread biases in human annotations linked to socio-demographic factors

Significant differences between human and LLM biases

Insights for designing less biased hate speech detection systems

Abstract

The rise of online platforms exacerbated the spread of hate speech, demanding scalable and effective detection. However, the accuracy of hate speech detection systems heavily relies on human-labeled data, which is inherently susceptible to biases. While previous work has examined the issue, the interplay between the characteristics of the annotator and those of the target of the hate are still unexplored. We fill this gap by leveraging an extensive dataset with rich socio-demographic information of both annotators and targets, uncovering how human biases manifest in relation to the target's attributes. Our analysis surfaces the presence of widespread biases, which we quantitatively describe and characterize based on their intensity and prevalence, revealing marked differences. Furthermore, we compare human biases with those exhibited by persona-based LLMs. Our findings indicate that while persona-based LLMs do exhibit biases, these differ significantly from those of human annotators. Overall, our work offers new and nuanced results on human biases in hate speech annotations, as well as fresh insights into the design of AI-driven hate speech detection systems.