HatePrototypes: Interpretable and Transferable Representations for Implicit and Explicit Hate Speech Detection

1. 1Identify existing hate speech benchmarks and their limitations
2. 2Develop new representations that capture implicit and indirect hate
3. 3Fine-tune models using these representations to improve detection accuracy
4. 4Evaluate and refine the models using transfer learning and interpretability metrics

AI engineers and researchers on a team can benefit from this research to improve hate speech detection models, while product managers can apply these findings to enhance content moderation systems

💡 Implicit hate speech detection requires novel representations that go beyond existing benchmarks

🚨 Improve hate speech detection with HatePrototypes! 🚨