## 1 Introduction
This gap in dataset availability limits the development of models capable of both verification and explainability. 2. Overemphasis on local explainability: Current explainable AFV systems focus predominantly on local explainability, explaining individual predictions, while neglecting global explainability, which is essential for understanding the system's overall decision-making logic [26]. This local focus leaves AFV systems lacking a holistic view of their behavior, limiting transparency and accountability. 3. Ambiguity in local and global explainability: There is a lack of consensus on how local and global explainability are defined and implemented in AFV systems. While local explainability focuses on individual prediction-level explanations, global explainability refers to understanding the model's overall reasoning process [30]. Differentresearchers interpret and apply these concepts inconsistently [1; 31; 32; 33], leading to confusion and retarded progress in explainable AFV research. 4. Inconsistencies in explainability taxonomy: There are discrepancies in how explainability concepts are categorized across studies in AFV. For instance, some researchers distinguish between intrinsic and post-hoc explainability [22], while others conflate interpretability with explainability, restricting it to individual explanations [18]. This lack of standardization creates further confusion in the field, hindering cohesive advancements in explainable AFV. In this research, we focus on addressing the first two critical gaps: the lack of explanation-focused datasets and the overemphasis on local explainability. To address these, we propose a comprehensive solution involving a novel explanation-focused dataset and a context-aware evidence-retrieval and explanation-generation methodology. In the subsequent sections, after providing the necessary background in Section 2, we describe the dataset in Section 3 and the methodology in Section 4. The dataset introduced is curated for XAI research in AFV. It pairs each claim with multiple annotated pieces of evidence within its thematic context (e.g., climate change, COVID-19, electric vehicles). The dataset facilitates both local and global explainability by enabling deeper exploration of claim-evidence relationships and thematic patterns extending beyond individual data points, while also supporting explainability-focused studies. Meanwhile, our context-aware retrieval methodology enhances the AFV pipeline, particularly the retrieval component, by incorporating thematic embeddings generated from a subset of the fact-verification dataset. This subset is identified through a statistical modeling approach and further refined through a semantic aggregation technique. By integrating broader contextual information with claim-specific embeddings, this methodology not only advances existing frameworks like RAG but also introduces a broader thematic context, resulting in more nuanced and context-sensitive explanations. The experimental framework of the methodology, including a case study and comparative analysis with RAG, is presented in Section 5, while challenges, future research directions, and conclusions are discussed in Sections 6-8, respectively. While our work contributes to mitigating these issues, the remaining two challenges, ambiguity in explainability terminologies and inconsistencies in explainability taxonomy, are expected to be gradually refined as more research in the field emerges, leading to greater clarity and standardization.