Academic

Trust Oriented Explainable AI for Fake News Detection

arXiv:2603.11778v1 Announce Type: new 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 improvi

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Krzysztof Siwek, Daniel Stankowski, Maciej Stodolski · · 1 min read · 7 views
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arXiv:2603.11778v1 Announce Type: new 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.

Executive Summary

The article explores the application of Explainable Artificial Intelligence (XAI) in Natural Language Processing (NLP) for fake news detection. It compares interpretability methods such as SHAP, LIME, and Integrated Gradients, finding that XAI enhances model transparency and interpretability while maintaining high detection accuracy. Each method offers distinct explanatory value, but limitations include computational cost and sensitivity to parameterization. The study demonstrates the effectiveness of integrating XAI with NLP for improving the reliability and trustworthiness of fake news detection systems.

Key Points

  • ▸ Explainable Artificial Intelligence (XAI) enhances model transparency and interpretability in fake news detection
  • ▸ SHAP, LIME, and Integrated Gradients provide distinct explanatory value for NLP models
  • ▸ XAI integration with NLP improves the reliability and trustworthiness of fake news detection systems

Merits

Improved Model Transparency

XAI techniques provide detailed insights into model decision-making processes, enhancing trust and reliability

Demerits

Computational Cost and Sensitivity

XAI methods can be computationally expensive and sensitive to parameterization, potentially limiting their practical application

Expert Commentary

The article presents a comprehensive analysis of XAI techniques in fake news detection, highlighting the benefits and limitations of each method. The findings demonstrate the potential of XAI to improve the reliability and trustworthiness of NLP models. However, further research is needed to address the computational cost and sensitivity of XAI methods. The study's results have significant implications for the development of effective fake news detection systems, and regulatory bodies should consider the adoption of XAI standards to ensure transparency and accountability.

Recommendations

  • ✓ Further research on optimizing XAI methods for computational efficiency and robustness
  • ✓ Development of standardized frameworks for XAI adoption in fake news detection systems

Sources

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