# Explainability of Automated Fact Verification Systems: A Comprehensive Review
## 3 Explainable AFV
### Data Perspective  
The potential of data explainability lies in its ability to provide deep insights that enhance the explainability of AI systems (which rely heavily on data for knowledge acquisition) [2; 9]. Data explainability methods encompass a collection of techniques aimed at \begin{table}
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**Methodological Aspect** & **Examples** & **Drawbacks** \\ \hline Summarization Approach & Ref. [23] utilizes the transformer model for extractive summarization. Two models are trained separately and jointly. Ref. [34] Fine-tuned for both extractive and abstractive summarization. & May generate misleading or inaccurate explanations. Particularly problematic for abstractive models. \\ \hline Logic-based Approach & Ref. [6] LOREN framework uses logical rules for transparency. Ref. [32] ProVer uses natural logic theorem & Complexity and computational cost limit scalability. May not capture all nuances of natural language. \\ \hline Attention-based Approach & Ref. [35] uses the attention mechanism to focus on salient words. Ref. [24] utilizes attention for feature and evidence highlighting. & Relies on human experts for visualizations, diverging from the principles of XAI. \\ \hline Counterfactual Approach & Ref. [39] focuses on interpretability by generating counterfactual explanations in AFV through question answering and entailment reasoning. & May face complexity in interpreting minimal input changes, particularly in intricate factual claims and evidence scenarios, potentially hindering the broader interpretability. \\ \hline \hline \end{tabular}
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Table 1: Comparative analysis of diverse methodologies employed in enhancing explainability in Automated Fact Verification systems. better comprehending the datasets used in the training and design of AI models [2]. The importance of a training dataset in shaping the behavior of AI models highlights the need to achieve a high level of data explainability. Therefore, it is crucial to note that constructing a high-performing and explainable model requires a high-quality training dataset. In AFV, the nature of this dataset, also known as the source of evidence, has evolved over time. Initially, the evidence was primarily based on claims, where information directly related to the claim was used for verification. Subsequently, knowledge-base-based approaches were introduced, utilizing structured knowledge sources to support the verification process. Further advances led to the adoption of text-based evidence, where relevant textual sources were used for verification. In recent developments, there has been a shift towards dynamically retrieved sentences, where the system dynamically retrieves and selects sentences that are most relevant to the claim for verification purposes. The subsequent text examines the implications of these changes through the lens of explainability. Systems such as [41] that process the claim itself, using no other source of information as evidence, can be termed as 'knowledge-free' or'retrieval-free' systems. In these systems, the linguistic characteristics of the claim are considered as the deciding factor. For example, claims that contain a misleading phrase are labeled 'Mostly False'. In [42], a similar approach is also employed, focusing on linguistic patterns, but a hybrid methodology is incorporated by including claim-related metadata with the input text to the deep learning model. These additional data include information such as the claim reporter's profile and the media source where the claim is published. These knowledge-free systems face limitations in their performance, as they depend only on the information inherent in the claim and do not consider the current state of affairs [43]. The absence of contextual understanding and the inability to incorporate external information make dataset-level explainability infeasible in these systems. In knowledge-base-based fact verification systems [44; 45; 46], a claim is verified against the RDF triples present in a knowledge graph.