Offensive Speech Detection

Subasa - Adapting Language Models for Low-resourced Offensive Language Detection in Sinhala

ARTICLE: Annotator Reliability Through In-Context Learning

Using LLMs to identify high-quality human annotators by checking if their labels are consistent with AI predictions—helping build better training data while preserving diverse …

Vicarious Offense and Noise Audit of Offensive Speech Classifiers: Unifying Human and Machine Disagreement on What is Offensive

We ran a massive experiment: 9 different AI content moderation systems analyzed 92 million YouTube comments about US politics. The results were shocking—different AI systems …