@article{ridoy-etal-2026-explainable,
    title = "Explainable {AI} for Ethical Counter Speech Generation in Hate Speech Mitigation",
    author = "Ridoy, Ashiful Islam  and
      Faisal, Mohammed  and
      Kumar, Yogesh  and
      Rashid, Md Mamun-Ur  and
      Ernst, Marina  and
      Hopfgartner, Frank",
    editor = "Piperidis, Stelios  and
      Bel, N{\'u}ria  and
      van den Heuvel, Henk  and
      Ide, Nancy  and
      Krek, Simon  and
      Toral, Antonio",
    journal = "International Conference on Language Resources and Evaluation",
    volume = "main",
    month = may,
    year = "2026",
    address = "Palma de Mallorca, Spain",
    publisher = "ELRA Language Resource Association",
    url = "https://preview.aclanthology.org/ingest-lrec/2026.lrec-main.165/",
    pages = "2104--2114",
    abstract = "The proliferation of hate speech in digital communication platforms poses significant challenges to online safety and social cohesion. While automated hate speech detection systems have shown promise, their black-box nature limits user trust and understanding of AI-driven content moderation decisions. This paper presents a framework that integrates explainable AI (XAI) techniques with counter-speech generation to create transparent, ethical solutions for hate speech mitigation. Our approach combines a fine-tuned HateBERT model, with a specialized Llama 3.1-8B-Instruct model for generating empathetic counter-narratives. The system employs five distinct XAI methods: Integrated Gradients, Attention Visualization, LIME, Counterfactual Analysis, and Natural Language Explanations to provide interpretable reasoning behind both detection and response generation decisions. The integration of explainability mechanisms with counter-speech generation represents a novel contribution to ethical AI systems, fostering transparency and trust in automated hate speech mitigation while maintaining high performance standards for real-world deployment."
}