# Explainability of Automated Fact Verification Systems: A Comprehensive Review
## 1 Introduction  
Advances in Artificial Intelligence (AI), particularly the transformer architecture [1] and the sustained success it has brought in transferring learning approaches in natural language processing, have led to advances in Automated Fact Verification (AFV). AFV systems are increasingly used in AI applications, making it imperative that AI-assisted decisions are also accompanied by reasoning, especially in sensitive sectors like medicine and finance [2]. In addition, researchers have also increasingly recognized the significance of AFV in the modern media landscape, where the rapid dissemination of information and misinformation has become a pressing concern [3]. Consequently, AFV systems have become pivotal in addressing the challenges posed by the spread of online misinformation, particularly in verifying claims and assessing their accuracy based on evidence from textual sources [4]. An AFV pipeline involves the sub tasks of collecting evidence related to a claim, sorting the most relevant evidence sentences, and predicting the veracity of the claim. Some systems such as [5] follow an additional step in the preliminary stage to detect whether a claim is check-worthy or not before commencing on the other sub tasksin the pipeline. Besides these sub tasks, recent studies like [6] have started exploring how to generate automatic explanations as the reason for veracity prediction. However, not as much effort has been put into the explanation functionality of AFV compared to the strong progress made over the past few years both in fact checking technology and datasets [7]. The lack of focus on explanation is behind the growing interest in explainable AI research [7]. Explainable AI is also known as interpretable AI or explainable machine learning. Although used interchangeably, there is a subtle difference between explainability and interpretability, where the latter is not necessarily easily understood by those with little experience in the field, unlike the former. Explainable AI aims to provide the reasoning behind the decision (prediction) made, in contrast to the 'black box' impression ([[https://en.wikipedia.org/wiki/Explainable_artificial_intelligence](https://en.wikipedia.org/wiki/Explainable_artificial_intelligence)]([https://en.wikipedia.org/wiki/Explainable_artificial_intelligence](https://en.wikipedia.org/wiki/Explainable_artificial_intelligence)), accessed on 15 September 2023) of machine learning, where even the AI practitioners fail to explain the reason behind a particular decision made by an AI system they designed. Similarly, the goal of explainable AFV systems is to go beyond simple fact verification by generating interpretations that are grounded in facts and that are communicated in a way that is easily understood and accepted by humans. Although there is broad agreement in the research community on the importance of the explainability of AI systems [8; 9; 10], there is much less agreement on the current state of explainable AFV. The latest studies on the verification of facts [11; 12; 13] do not cohere around an aligned view on the subject, while researchers like [12] state that \"Modern fact verification systems have distanced themselves from the black-box paradigm\". The authors of [13] contradict this by stating that modern AFV systems estimate the truthfulness \"using numerical scores which are not human-interpretable\". The same impression, as articulated in the latter statement, can be drawn from the literature review of state-of-the-art AFV systems. Another of the most recent arguments supporting this view is [11]. They assert that, despite being a \"nontrivial task\", the explainability of AFV is \"mostly unexplored\" and \"needs to evolve\" compared to the developments in explainable NLP. This issue is further exacerbated by the fact that providing justifications for claim verdicts has always been a crucial aspect of human fact checking [3; 7].