Towards Smart Fake News Detection Through Explainable AI
This work tackles the problem of improving transparency in fake news detection for users and platforms, but it appears incremental as it builds on existing explainable AI approaches.
The paper addresses the lack of explanation capabilities in AI-based fake news detection systems by reviewing current state-of-the-art methods and presenting ongoing research on a multi-modal explainable model.
People now see social media sites as their sole source of information due to their popularity. The Majority of people get their news through social media. At the same time, fake news has grown exponentially on social media platforms in recent years. Several artificial intelligence-based solutions for detecting fake news have shown promising results. On the other hand, these detection systems lack explanation capabilities, i.e., the ability to explain why they made a prediction. This paper highlights the current state of the art in explainable fake news detection. We discuss the pitfalls in the current explainable AI-based fake news detection models and present our ongoing research on multi-modal explainable fake news detection model.