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New explainability method for BERT-based model in fake news detection.

Mateusz Szczepański1,2, Marek Pawlicki1,2, Rafał Kozik1,2

  • 1ITTI Sp. z o.o., Poznań, Poland.

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Summary
This summary is machine-generated.

This study introduces a new explainability method for AI fake news detectors, enhancing trust in AI systems. The approach uses explainable artificial intelligence (xAI) techniques to understand how these detectors identify disinformation.

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Area of Science:

  • Artificial Intelligence
  • Natural Language Processing
  • Social Media Analysis

Background:

  • Social media's pervasive influence enables rapid information exchange but also facilitates the spread of disinformation ('Fake News').
  • Existing countermeasures for fake news detection often rely on Natural Language Processing (NLP) and Deep Learning (DL) models.
  • High model performance in fake news detection is insufficient; explainability of AI decisions is critical for real-world applications.

Purpose of the Study:

  • To present a novel explainability approach for BERT-based fake news detectors.
  • To offer an extension that can be easily integrated with existing fake news detection systems.
  • To evaluate the effectiveness of explainable artificial intelligence (xAI) techniques for fake news detection.

Main Methods:

  • Utilizing two explainable artificial intelligence (xAI) techniques: Local Interpretable Model-Agnostic Explanations (LIME) and Anchors.
  • Applying these techniques to BERT-based models for fake news detection.
  • Evaluating the explainability approach on short text data, such as tweets and headlines.

Main Results:

  • The proposed explainability approach provides insights into the decision-making process of BERT-based fake news detectors.
  • LIME and Anchors were successfully applied as extensions to enhance the interpretability of fake news detection models.
  • The evaluation demonstrated the feasibility of integrating xAI techniques without major system modifications.

Conclusions:

  • Explainability is a crucial component for trustworthy AI in fake news detection.
  • The presented approach offers a practical method to improve the transparency of fake news detectors.
  • Further research can build upon this work to develop more robust and interpretable AI systems for combating disinformation.