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Trust Oriented Explainable AI for Fake News Detection

Krzysztof Siwek, Daniel Stankowski, Maciej Stodolski

Abstract

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.

Trust Oriented Explainable AI for Fake News Detection

Abstract

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.
Paper Structure (25 sections, 4 equations, 4 figures, 3 tables)

This paper contains 25 sections, 4 equations, 4 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Mechanisms and Manifestations
  4. Neural Networks in NLP
  5. Recurrent Networks
  6. Convolutional Neural Networks
  7. Transformers
  8. XAI as a tool for interpreting NLP models
  9. SHAP - Shapley Additive Explanations
  10. LIME - Local Interpretable Model agnostic Explanations
  11. Integrated Gradients Method
  12. Risk of Misinterpretation
  13. Analysis of Fake News Detection Models
  14. Data acquisition and processing
  15. Feature Engineering and Text Vectorization
  16. ...and 10 more sections

Figures (4)

  • Figure 1: Model architecture: a) LSTM, b) CNN
  • Figure 2: SHAP visualization for CNN model
  • Figure 3: LIME visualization for CNN model
  • Figure 4: Integrated Gradients visualization for CNN model