# Explainability of Automated Fact Verification Systems: A Comprehensive Review
## 2 Explainable Artificial Intelligence  
The field of interpretability in Artificial Intelligence is experiencing rapid growth, with numerous studies [2; 10; 15] exploring different facets of interpretation. These investigations are often conducted under the umbrella of explainable AI (XAI), which encompasses various approaches and methodologies aimed at providing explanations for the decision-making processes of black-box AI models, providing information on how they generate their outcomes. This section reviews the pertinent literature on XAI, with three main objectives: What is explainability? Why is it needed? How can it be implemented? The primary objective of XAI is to build models that humans can interpret effectively, especially in sensitive sectors like military, banking, and healthcare. These domains rely on the expertise of specialists to solve problems more efficiently, while also seeking meaningful outputs to understand and trust the solutions provided [16]. Additionally, it benefits both domain specialists and developers when appropriate outputs are available, as it encourages investigation into the system when discrepancies occur. However, many AI systems that support decision making are developed as opaque structures, often referred to as 'black-box' models, which conceal their internal logic from the user and raise practical and ethical concerns [9; 17]. These black-box models, while typically offering higher accuracy, do so at the cost of transparency, creating an inherent tension with the need for explainability in machine learning systems [15]. In contrast, white-box models are deliberately designed to be interpretable, which makes their outputs easier to understand, but the drawback of these models is the compromise on accuracy. Gray-box models attempt to strike a balance between interpretability and accuracy, offering a favorable trade-off [2]. Figure 1 demonstrates a comparison of these models and briefly conveys the idea of explainability in terms of AI systems. However, it is worth acknowledging that in certain scenarios, such as those involving structured data with naturally meaningful features, simpler classifiers, such as logistic regression or decision lists, may produce competitive results after appropriate preprocessing, as emphasized in the work by [18]. Upon investigating the reasons for the increase in popularity of this research field, it is evident that XAI has received increasing attention from both academia and industry [2; 7; 8; 10], exhibiting an inflection point in the middle of the last decade [15]. This paragraph presents a brief analysis of the factors contributing to this surge in research interest in an attempt to resolve why explainability is important and continues to be a pressing requirement in AFV or AI in general. According to studies [2; 19], as AI becomes more widely implemented, concerns about AI's black-box working paradigm have also become prevalent among governments and the general public. This has led to the need for regulatory authorities to push for some form of explainability. An initial step towards this AI regulation was taken by the European Parliament in 2016, when it adopted the General Data Protection Regulation (GDPR) (Regulation (EU) 2016/679 of the European Parliament and of the Council, of 27 April 2016, uri=CELEX:32016R0679 ). With the GDPR policy requiring citizens to receive explanations for algorithmic decisions, explainability became a significant aspect of algorithm design thereafter [2; 15; 20]. Another authoritative supervision on XAI practices was by the R&D Agency of the United States Department of Defense ([[https://www.darpa.mil/program/explainable-artificial-intelligence](https://www.darpa.mil/program/explainable-artificial-intelligence)]([https://www.darpa.mil/program/explainable-artificial-intelligence](https://www.darpa.mil/program/explainable-artificial-intelligence)), accessed on 20 September 2023), the Defense Advanced Research Projects Agency (DARPA) [21].