Trust Oriented Explainable AI for Fake News Detection
This work addresses the need for trustworthy fake news detection systems, but it is incremental as it compares existing XAI techniques without introducing new methods.
The study applied Explainable AI (XAI) methods like SHAP, LIME, and Integrated Gradients to NLP-based fake news detection, finding that they enhance model transparency and interpretability while maintaining high detection accuracy.
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 Gradients performs efficiently with convolutional models. The study also highlights limitations such as computational cost and sensitivity to parameterization. Overall, the findings demonstrate that integrating XAI with NLP is an effective approach to improving the reliability and trustworthiness of fake news detection systems.