# Explainability of Automated Fact Verification Systems: A Comprehensive Review
## 3 Explainable AFV
### Data Perspective
The veracity of the claim is calculated by assessing the error between the claim and the triples based on different approaches such as rule-based, subgraph-based, or embedding-based methods. The drawback of such systems is the likelihood of a claim being verified as false, based on the assumption that the supporting facts of a true claim are already present in the graph, which is not always feasible. This limited scalability and the inability to capture nuanced information hinder the achievement of explainability in these types of fact verification models. Unlike the latter two approaches, in the evidence retrieval approach, supporting pieces of evidence for the claim verdict have to be fetched from a relevant source using an information retrieval method. Although the benefits of such systems outweigh the limitations of static approaches mentioned earlier, there are certain significant constraints that can also affect the explainability of these models. While the quality of the source (biased or unreliable), availability of the source (geographical or language restrictions), and resources for the retrieval process (time-consuming and expensive human and computational resources) can have a significant impact on the evidence retrieval and limit the scope of evidence, a deep understanding of the claim's context is critical to avoid misinterpreted and incomplete evidence which leads to erroneous verdicts. Nevertheless, these limitations suggest that the evidence retrieval approach might not be entirely consistent with key XAI principles such as 'Accuracy' and 'Fidelity'. This, in turn, casts doubt on the effectiveness of any post hoc explainability measures attempted within this data aspect. An alternative approach is using text from verified sources of information as evidence; encyclopedia articles, journals, Wikipedia, and fact-checked databases are some examples. Since Wikipedia is an open-source web-based encyclopedia and contains articles on a wide range of topics, it is consistently considered an important source of information for many applications, including economic development [47], education [48], data mining [49], and AFV. For example, the FEVER task [26], an application in AFV, relies on the retrieval of evidence from Wikipedia pages. In the FEVER dataset, each SUPPORTED/REFUTED claim is annotated with evidence from Wikipedia. This evidence could be a single sen tence, multiple sentences, or a composition of evidence from multiple sentences sourced from the same page or multiple pages of Wikipedia. This approach aligns well with the XAI principle of 'Interpretability', as Wikipedia is a widely accessible and easily understandable source of information. However, it is crucial to note that Wikipedia also comes with limitations that could impact the 'Accuracy' and 'Fidelity' principles of XAI, which can potentially impact the interpretability of models relying on Wikipedia as a primary data source. Firstly, like any other source, Wikipedia pages can contain biased and inaccurate content, and these can remain undetected for a longer period (the same goes for outdated information); this compromises the 'Accuracy' of any AFV model trained on these data. Secondly, despite covering a wide range of topics, Wikipedia suffers deficiencies in comprehensiveness ([[https://en.wikipedia.org/wiki/Reliability_of_Wikipedia#Coverage](https://en.wikipedia.org/wiki/Reliability_of_Wikipedia#Coverage)]([https://en.wikipedia.org/wiki/Reliability_of_Wikipedia#Coverage](https://en.wikipedia.org/wiki/Reliability_of_Wikipedia#Coverage)), accessed on 15 September 2023), limiting a model's ability to understand contextual information fully, thereby affecting 'Interpretability'. Lastly, models trained predominantly on Wikipedia's textual content can develop biases and limitations inherent to the nature and scope of Wikipedia's content, impacting both 'Fidelity' and 'Interpretability' when applied to diverse real-world scenarios and varied types of unstructured data.