# Explainability of Automated Fact Verification Systems: A Comprehensive Review
## 5 Future Research Directions  
In addition to the future plans outlined in the limitations of this study, the following directions for exploration and research are proposed. * Direction 1: Exploring a Balanced Approach to Explainability in AFV: Researchers should explore the development of techniques and methodologies aimed at achieving a balanced approach to explainability, integrating both global and local perspectives into AFV systems. This involves understanding the broader relationships and patterns that underlie AFV model decisions across diverse factual claims and evidence (global explainability), while also addressing the specific concerns related to individual instances (local explainability). For instance, a nuanced exploration of gray explainability could involve refining gray-box models to optimize the trade-off between interpretability and accuracy, ensuring that the explanations provided are as meaningful and understandable as possible without incurring a substantial loss in predictive accuracy. Although examples such as dispute resolution and individual patient treatment decisions illustrate the broader applicability and importance of this approach beyond the realm of fact verification, they underscore the universal need for tailored and comprehensible explanations in individual cases. In fact verification systems, a balanced approach is particularly crucial for gaining both a localized understanding of individual claims and a broader insight that can inform strategies for handling diverse types of information and evidence. By investigating methods that provide insights into AFV model behavior and reasoning patterns on both the macro and micro levels, researchers can work towards achieving a holistic understanding of explainability in AFV systems. Following the principles of XAI, a potential starting point could be to explain multiple representative individual predictions (locally) as a means to gain insights towards a more comprehensive understanding, as suggested in [25]. This nuanced exploration, which aligns with the overarching goal of achieving explainability in AFV systems, ensures that the insights gained are as widely applicable as they are individually relevant, potentially leading to more informed and equitable decision-making processes across different domains. * Direction 2: Comprehensive Investigation and Comparative Analysis of AFV Datasets: Future research endeavors could benefit from undertaking a meticulous and comprehensive review of the applicable datasets for AFV, informed by the insights provided in Tables 1 and 2. Table 1 outlines a comparative analysis of various methodologies used to improve explainability in AFV systems, while Table 2 delves into the distinctive attributes and inherent diversity within various types of datasets in AFV. A focused study in this direction could reveal deeper insights into the suitability and compatibility of various datasets with different AFV models and explainability techniques, providing a more nuanced understanding of the interactions between dataset characteristics and explainability. Such an investigation would not only enrich the understanding of the influence of diverse datasets on the explainability of AFV models but also reveal untapped potential in utilizing underexplored types of dataset to enhance model transparency and interpretability. By synergizing the diverse techniques for explainability and the variety of dataset types highlighted in the tables, this research direction has substantial potential to reduce the gap in the field of explainable AFV. * Direction 3: Development of an Explainability-Focused, Explanation-Learning-Friendly (ELF) Dataset: As a logical progression from Direction 2, researchers should prioritize developing an ELF dataset to address the lack of explanations in existing AFV datasets, enabling more nuanced studies in explainability in AFV.