## 1 Introduction  
Explainability, defined as the ability to interpret model behavior in a human-understandable way [1], is increasingly essential in AI applications such as Automated Fact Verification (AFV) systems. While recent advancements in AI architectures, such as transformer models [2] and Retrieval-Augmented Generation (RAG) [3], have significantly expanded AFV capabilities, they also pose new challenges in ensuring that system decisions remain transparent and interpretable to end-users and decision-makers. Most AFV systems generally follow a three-stage pipeline architecture, similar to that used in the Fact Extraction and Verification (FEVER) shared task [4]. This architectural approach was later adopted by several subsequent researchers such as [4; 5; 6; 7; 8; 9; 10]. This architecture involves the composite tasks of collecting or retrieving relevant evidence to support or refute a claim, ranking these pieces of evidence by importance, and predicting the claim's veracity, inter alia. However, as AFV systems incorporate more advanced architectures and methods, ensuring interpretability in their increasingly complex decision-making processes has become essential. This need is particularly critical in AFV systems, since online misinformation has become ubiquitous in recent times [11]. Furthermore, explainability is becoming increasingly important in critical domains such as finance, healthcare, and journalism, and is supported by a growing body of research in Explainable AI (XAI) [12; 13; 14; 15]. This emphasis on explainability is also supported by government-led initiatives, such as the European Union's General Data Protection Regulation (GDPR), which mandates explanations for algorithmic decisions, and the United States Department of Defense's (DARPA) XAI program, which aims to make AI systems more interpretable and trustworthy, and highlights the need for greater transparency in AI systems [12; 13; 16; 17]. However, despite notable advancements in XAI, AFV technologies and the availability of diverse fact-verification datasets, the integration of XAI within AFV remains limited. Some studies have attempted to incorporate XAI into AFV systems, although these approaches exhibit certain limitations. For instance, transformer-based models for extractive and abstractive summarization [18; 19] risk producing incomplete or misleading explanations. Logic-based models like LOREN [20] and ProoFver [10] create transparent explanations through logic rules but are difficult to scale and apply in real-world contexts. Attention mechanisms [21; 22; 23] highlight important features, yet their reliability is questionable as attention scores do not consistently align with key decision-making features [11]. Counterfactual explanations [24; 25] demonstrate how small input changes affect predictions, but interpreting them remains challenging in complex fact-checking scenarios. Some of these approaches have outperformed the state-of-the-art in claim veracity prediction [10], but they remain limited in terms of scope and effectiveness for explainability. In addition to the limitations discussed, notable summative studies in this cross-domain of XAI-AFV, such as the comprehensive review on explainable AFV by [15] and the extensive survey on explainable automated fact-checking by [26], bring attention to several persistent gaps. Addressing these gaps would not only enhance explainability within AFV systems but also help mitigate the limitations identified in existing approaches. The following items summarize and discuss some pertinent aspects of explainability in the context of existing approaches. 1. Lack of explanation-focused datasets: Existing fact-verification datasets like FEVER [27] and MultiFC [28] are not designed to support explanation learning aligned with XAI standards. There is a need for datasets that facilitate training models not only to verify facts, but also to generate meaningful explanations, as previously noted by [29].