# Explainability of Automated Fact Verification Systems: A Comprehensive Review
## 4 Discussion
[23] discusses the explainability as providing local explanations for individual data points, without specifically addressing local or global aspects. As a result, the definitions of local coherence, global coherence, and explainability in AFV studies predominantly refer to prediction-level explainability, leaving the concept of global explainability in AFV insufficiently defined. The lack of recognition of the importance of global explainability is evident in implementations as well. Existing systems primarily focus on local explainability, which hampers an adequate understanding of a system's decision-making process on the dataset level. In an extensive survey conducted by [7] on explainable AFV systems, it was found that all the examined systems focused primarily on providing explanations for individual predictions rather than offering explanations about the underlying fact-checking model itself. This indicates a prevalent trend in the field of explainable AFV, where the emphasis is on local explainability. However, this local explainability is not sufficient for AFV systems because it only provides insights into individual predictions without offering a holistic view of the system's overall behavior. Furthermore, global explainability is crucial for AFV systems, as it provides a comprehensive understanding of how the system arrives at its predictions and decisions. This global approach also allows AFV systems to align with advances in XAI research and comply with the XAI principles, enabling transparency and accountability. In addition to the ambiguity surrounding local and global concepts, the field of explainable AFV is further complicated due to variations in how explainability concepts are categorized, suggesting a lack of consensus on taxonomy. For example, while [24] distinguishes between intrinsic explainability and post hoc explainability, other researchers in explainable AFV, such as [23], propose a categorization that broadly divides XAI into interpretability and explainability. In [24], intrinsic explainability is described as the process of creating self-explanatory models that inherently incorporate explainability. This suggests that their definition of 'intrinsic explainability' closely aligns with the general notion of 'interpretability' related to model-level reasoning, as discussed in Section 2.3. However, the choice of the term 'intrinsic' in [24] adds a distinct nuance to this categorization. On the other hand, their view on post hoc explainability is in line with standard XAI. In contrast, while ref. [23] aligns 'interpretability' with the mainstream XAI taxonomy, they adopt a narrower view for 'explainability', reserving it for local explanations of individual instances, which is a subset of post hoc explainability. This deviates from the wider view, where 'explainability' usually refers to model-level justifications. These disparities in taxonomy demonstrate that the ambiguity extends beyond the local and global dimensions, contributing to the overall ambiguity within the field of explainable AFV. This disagreement and discrepancy among the relatively few existing explainable AFV systems pose significant challenges for the growth and advancement of research in this field and highlight the need for a more standardized approach to explainability in AFV systems.