# Explainability of Automated Fact Verification Systems: A Comprehensive Review
## 3 Explainable AFV
### Methodological Perspective
#### 3.2.4 Counterfactual Approach  
Counterfactual explanations, also known as inverse classifications, describe minimal changes to input variables that would lead to an opposite prediction, offering the potential for recourse in decision-making processes [18]. These explanations allow users to understand what modifications are needed to reverse a prediction made by a model. In the context of AFV, counterfactual explanations have been explored. The study in [39], for example, explicitly focuses on the interpretability aspect of counterfactual explanations in order to help users understand why a specific piece of news was identified as fake. The comprehensive method introduced in that work involves question answering and entailment reasoning to generate counterfactual explanations, which could enhance users' understanding of model predictions in AFV. In a recent study [40] exploring debiasing for fact verification, researchers propose a method called CLEVER that operates from a counterfactual perspective to mitigate biases in predicting the veracity. CLEVER stands out by training separate models for claim-evidence fusion and claim-only prediction, allowing the unbiased aspects of predictions to be highlighted. This method could be explored further in the context of explainability in AFV, as it allows users to discern the factors that lead to specific predictions, even if the main emphasis of the cited work was on bias mitigation.  
Nevertheless, counterfactual explanations in AFV, while providing valuable insights into why a model makes specific predictions, may also face challenges in their practical application. One notable limitation lies in the potential complexity and difficulty of interpreting minimal changes in input variables, especially in cases involving complex facts and evidence. This complexity could pose challenges to users in grasping the precise factors that influence the predictions of the models, which is a key aspect in achieving a broader interpretability in AI systems, as discussed in Section 2.  
Table 1 categorizes the existing approaches to develop explainable AFV systems into the four methodological aspects discussed: summarization, logic-based, attention-based, and counterfactual. Each category is illustrated with examples of studies that employ these methods, highlighting their unique contributions as well as potential drawbacks. The table serves as a comprehensive overview, aiding in understanding the various techniques used to enhance the explainability and interpretability in state-of-the-art AFV systems.  
Additionally, it is important to note the inherent complexity in typical DNN-based AFV systems. When considered alongside the objectives of XAI outlined in Section 2.1, which emphasize that the interpretability of a predictive model is often assessed through its complexity (commonly measured by its size), this factor adds another layer of complexity to the already challenging task of achieving model explainability in state-of-the-art AFVs. However, the situation is equally challenging when it comes to post hoc explainability, especially in terms of achieving global explainability. None of the explainable AFV systems discussed provide global explainability; they mainly focus on prediction-level or local explainability by explaining the model's decision-making process for specific instances or cases. On the other hand, global interpretability at the dataset level aims to uncover more general relationships learned by the model and provides a greater understanding of how the model learns and generalizes across different examples [22]. The following section explores the extent of dataset-level explainability in AFV, leading to an examination of its current state.