Hate Speech Detection

Hate speech detection is a natural language processing task focused on automatically identifying text that contains hateful, abusive, or discriminatory language targeting individuals or groups based on characteristics like race, gender, religion, or sexuality. Modern hate speech detection systems use transformer-based models fine-tuned on labeled datasets, leveraging techniques like few-shot prompting and transfer learning to handle the nuanced and evolving nature of harmful language. These systems are deployed at scale by social media platforms, content moderation services, and online communities to maintain safe digital spaces. Key challenges include distinguishing hate speech from sarcasm or reclaimed language, handling multilingual content, and avoiding bias in the training data. Hate speech detection is a critical component of responsible AI and trustworthy AI initiatives, requiring continuous human-in-the-loop evaluation to adapt to new forms of harmful expression.