Trust Oriented Explainable AI for Fake News Detection
This article examines the application of Explainable Artificial Intelligence (XAI) in NLP based fake news detection and compares selected interpretability methods. The work outlines key aspects of disinformation, neural network architectures, and XAI techniques, with a focus on SHAP, LIME, and Integrated Gradients. In the experimental study, classification models were implemented and interpreted using these methods. The results show that XAI enhances model transparency and interpretability while maintaining high detection accuracy. Each method provides distinct explanatory value: SHAP offers detailed local attributions, LIME provides simple and intuitive explanations, and Integrated Gradi...
| Authors | Krzysztof Siwek, Daniel Stankowski, Maciej Stodolski |
|---|---|
| Primary category | cs.CL |
| Published | 12 March 2026 |
| Last updated at source | 12 March 2026 |
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