用可解释AI提升假新闻检测的可信度，让模型决策更透明。
Trust Oriented Explainable AI for Fake News Detection
- 对比SHAP、LIME和梯度积分法，评估其在假新闻检测中的解释能力。
- 三种方法均保持高准确率，其中梯度积分对卷积模型效率最高。
- 适合关注模型可信度的研究者与内容安全应用开发者。
本文研究基于自然语言处理的假新闻检测中可解释人工智能（XAI）的应用，比较了SHAP、LIME和集成梯度等可解释性方法。研究梳理了虚假信息特征、神经网络架构及XAI技术，并在实验中实现分类模型并进行解释分析。结果表明，引入XAI能显著提升模型透明度与可解释性，同时维持高检测准确率。其中，SHAP提供细致的局部贡献分析，LIME生成直观简洁的解释，集成梯度在卷积模型上计算高效。研究也指出计算开销大和参数敏感性等局限。总体表明，将XAI与NLP结合是提升假新闻检测系统可靠性与可信度的有效路径。
原文摘要 · Abstract (English)
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.
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