The rapid growth in Artificial Intelligence (AI) has led to considerable progress in Automated Fact Verification (AFV). This process involves collecting evidence for a statement, assessing its relevance, and predicting its accuracy. Recently, research has begun to explore automatic explanations as an integral part of the accuracy analysis process. However, the explainability within AFV is lagging compared to the wider field of explainable AI (XAI), which aims at making AI decisions more transparent. This study looks at the notion of explainability as a topic in the field of XAI, with a focus on how it applies to the specific task of Automated Fact Verification. It examines the explainability of AFV, taking into account architectural, methodological, and dataset-related elements, with the aim of making AI more comprehensible and acceptable to general society. Although there is a general consensus on the need for AI systems to be explainable, there a dearth of systems and processes to achieve it. This research investigates the concept of explainable AI in general and demonstrates its various aspects through the particular task of Automated Fact Verification. This study explores the topic of faithfulness in the context of local and global explainability. This paper concludes by highlighting the gaps and limitations in current data science practices and possible recommendations for modifications to architectural and data curation processes, contributing to the broader goals of explainability in Automated Fact Verification.

The rapid growth of Artificial Intelligence (AI) has led to considerable progress in Automated Fact Verification (AFV). This process involves collecting evidence for a statement, assessing its relevance, and predicting its accuracy. Recently, research has begun to explore automatic explanations as an integral part of the accuracy analysis process. However, the explainability within AFV is lagging compared to the wider field of explainable AI (XAI), which aims at making AI decisions more transparent. This study looks at the notion of explainability as a topic in the field of XAI, with a focus on how it applies to the specific task of Automatic Fact Verification. It examines the explainability of AFV, taking into account architectural, methodological, and dataset-related elements, with the aim of making AI more comprehensible and acceptable to the general society. Although there is a general consensus on the need for AI systems to be explainable, there a dearth of systems and processes to achieve it. In this research, we investigate the concept of explainable AI in general and demonstrate its various aspects through the particular task of Automated Fact Verification. We explore topic of faithfulness in the context of local and global explainability and how these correspond with architectural, methodological and data-based ways of achieving it. We examine these concepts for the specific case of AFV and analyze the current datasets used for AFV and how they can be adapted to further the identified aims on XAI. The paper concludes by highlighting the gaps and limitations in current data science practices and possible recommendations for modifications in architectural and data curation processes that would further the goals of XAI.

This study introduces an explainable framework for Automated Fact Verification (AFV) systems, integrating a novel Context-Aware Retrieval and Explanation Generation (CARAG) methodology. CARAG enhances evidence retrieval by leveraging thematic embeddings derived from a Subset of Interest (SOI, a focused subset of the fact-verification dataset) to integrate local and global perspectives. The retrieval process combines these thematic embeddings with claim-specific vectors to refine evidence selection. Retrieved evidence is integrated into an explanation-generation pipeline employing a Large Language Model (LLM) in a zero-shot paradigm, ensuring alignment with topic-based thematic contexts. The SOI and its derived thematic embeddings, supported by a visualized SOI graph, provide transparency into the retrieval process and promote explainability in AI by outlining evidence-selection rationale. CARAG is evaluated using FactVer, a novel explanation-focused dataset curated to enhance AFV transparency. Comparative analysis with standard Retrieval-Augmented Generation (RAG) demonstrates CARAG’s effectiveness in generating contextually aligned explanations, underscoring its potential to advance explainable AFV frameworks.

This study introduces an explainable framework for Automated Fact Verification (AFV) systems, integrating a novel Context-Aware Retrieval and Explanation Generation (CARAG) methodology. CARAG enhances evidence retrieval by leveraging thematic embeddings derived from a Subset of Interest (SOI, a focused subset of the fact verification dataset), to integrate local and global perspectives. The retrieval process combines these thematic embeddings with claim-specific vectors to refine evidence selection. Retrieved evidence is integrated into an explanation generation pipeline employing a Large Language Model (LLM) in a zero-shot paradigm, ensuring alignment with topic-based thematic contexts. The SOI and its derived thematic embeddings, supported by a visualized SOI graph, provide transparency into the retrieval process and promote explainability in AI by outlining evidence selection rationale. CARAG is evaluated using FactVer, a novel explanation-focused dataset curated to enhance AFV transparency. Comparative analysis with standard Retrieval-Augmented Generation (RAG) demonstrates CARAG’s effectiveness in generating contextually aligned explanations, underscoring its potential to advance explainable AFV frameworks.