# Explainability of Automated Fact Verification Systems: A Comprehensive Review
## 3 Explainable AFV
### Data Perspective
Given these considerations and their misalignment with the XAI objectives of 'Interpretability', 'Accuracy', and 'Fidelity', it becomes evident that relying solely on Wikipedia as a training dataset may not be the most effective pathway toward explainable AFV. Alternatively, Wikipedia can be used as an elementary corpus to train the AI model to achieve a general understanding of various knowledge domains for AFV, and this background or prior knowledge can then be harnessed further with additional domain data to gain a deeper context (which helps the model to attain information on global relationships and thus increase explainability). As the largest Wikipedia-based benchmark dataset for fact verification [28, 50], the FEVER dataset can unarguably be considered as this elementary corpus for AFV tasks, and transformers and transfer learning is the most pragmatic technology choice for AFV according to state-of-the-art systems [31, 32, 51]. The quality of the dataset used or created for an application is a major factor in determining the explainability of a transformer-based AFV model and its ability to comprehend the underlying context. For example, ref. [52] developed the SCIFACT dataset in order to expand the ideas of FEVER to COVID-19 applications. SCIFACT comprises 1.4 K expert-written scientific claims along with 5K+ abstracts (from different scientific articles) that either support or refute each claim and are annotated with rationales, which consist of a minimal collection of sentences from the abstract that imply the claim. This study demonstrated the obvious advantages of using such a domain-specific dataset (it can also be called a subdomain here as scientific claim verification is a sub task of claim verification) as opposed to just using a Wikipedia-based evidence dataset. In [52], it is argued that the inclusion of rationales in the training dataset \"facilitates the development of interpretable models\" that not only label predictions but also identify the specific sentences necessary to support the decisions. However, the limited scale of the dataset, consisting of only 1.4 K claims, necessitates caution in interpreting assessments of system performance and underscores the need for more expansive datasets to propel advancements in explainable fact checking research. Building on this perspective of improving the quality and diversity of the dataset, ref. [53] critically evaluated the FEVER corpus, emphasizing its reliance on synthetic claims from Wikipedia and advocating for a corpus that incorporates natural claims from a variety of web sources. In response to this identified need, they introduced a new, mixed-domain corpus, which includes domains like blogs, news, and social media--the mediums often responsible for the spread of unreliable information. This corpus, which encompasses 6422 validated claims and over 14,000 documents annotated with evidence, addresses the prevalent limitations in existing corpora, including restricted sizes, lack of detailed annotations, and domain confinement. However, through a meticulous error analysis, ref. [53] discovered inherent challenges and biases in claim classification attributed to the heterogeneous nature of the data and the incorporation of fine-grained evidence (FGE) from unreliable sources. These findings illustrate substantial barriers to realizing the fundamental goals of XAI, particularly accuracy and fidelity. Moreover, ref. [53]'s focus on diligently modeling meta-information related to evidence and claims could be understood as their implicit recognition of the crucial role of explainability in the realm of automated fact checking. By suggesting the integration of diverse forms of contextual information and reliability assessments of sources, they highlight the necessity of developing models that are not only more accurate but also capable of providing reasoned and understandable decisions, a pivotal step towards fostering explainability in automated fact checking systems.