# Explainability of Automated Fact Verification Systems: A Comprehensive Review
## 4 Discussion  
While fact checking datasets commonly support the standard three-stage pipeline of fact verification, there is currently a lack of datasets that specifically facilitate explanation learning aligned with the government and intergovernmental standards on XAI. This is of paramount importance in explainable AFV; if an AI system is expected to produce explanations, it should have the ability or opportunity to consume explanations. To achieve this, it is necessary to train a model network using an explanation-learning-friendly (ELF) dataset. However, prominent large-scale datasets like FEVER [54] and MultiFC [55] lack this aspect of the fact verification task. Furthermore, currently there are no alternative resources available to address this limitation, as commented on in [56]. To create an ELF dataset, it is essential to analyze the dataset practices of fact verification systems with a focus on explainability. This paper has undertaken this crucial initial step and found that the absence of an explanation-based fact verification corpus presents a significant obstacle to advancing research in the field of explainable fact checking. In addition to the lack of suitable ELF datasets in AFV, another significant challenge to the growth of the explainable AFV field is the ambiguity and discrepancies surrounding the concepts of local and global explainability. Global interpretability refers to the ability to comprehend the overall logic and reasoning of a model, including all possible outcomes. It involves understanding the complete decision-making process and the underlying principles of the model. On the other hand, local interpretability refers to the ability to understand the specific reasons or factors that contribute to a particular decision or prediction. It focuses on the interpretability of individual predictions or decisions rather than the entire model. These terms are not consistently understood and implemented in different research communities, leading to confusion and slowing progress in the field. Although XAI researchers [9; 16; 17; 22] generally agree on the definitions of local and global explainability, explainable AFV researchers have different interpretations and perspectives, contributing to the ambiguity surrounding explainable AFV. For example, ref. [34] focuses on local coherence and global coherence, evaluating sentence cohesion and the appropriateness of explanations in relation to the claim and associated label, both at the \begin{table}
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**Fact\_Verification Dataset Type** & **Example\_Studies** & **Knowledge\_Type** & **Text\_Type** & **Domain\_Type** & **Remarks** \\ \hline \hline Knowledge-free Systems & & **Knowledge-Free** & **External Knowledge** & **Structured Data** & **Free Text** & **Single Domain** & **Multi Domain** & \\ \hline Knowledge-free Systems & [41; 42] & ✓ & \(\times\) & – & – & – & – & Lack of contextual understanding; dataset-level explainability \\ \hline Knowledge-Base-Based & [44; 45] & \(\times\) & \(\times\) & ✓ & \(\times\) & ✓ & ✓ & Limited scalability; inability to capture nuanced information \\ \hline Wikipedia-Based & [54] & \(\times\) & ✓ & \(\times\) & ✓ & \(\times\) & ✓ & \(\times\) & ✓ \\ \hline Domain (Single)-Specific Corpus & [52] & \(\times\) & ✓ & \(\times\) & ✓ & ✓ & \(\times\) & Limited size; potential for biased evaluation \\ \hline Mixed-domain Corpus & [53] & \(\times\) & ✓ & \(\times\) & ✓ & \(\times\) & ✓ & Challenges in classification due to heterogeneous data (impacts accuracy); evidence from unreliable sources (impacts fidelity) \\ \hline \hline \end{tabular} Note: In this table (✓) indicates ’Yes’ or ’Applicable’, (\(\times\)) indicates ’No’ or ’Not Applicable’, and (\(-\)) signifies the absence of a particular attribute or feature.  
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Table 2: Comparative analysis of dataset types and their impact on explainability in AFV systems. prediction level. On the other hand, ref.