# Explainability of Automated Fact Verification Systems: A Comprehensive Review
## 2 Explainable Artificial Intelligence
The DARPA regulated an XAI research program and funded 11 research groups from the USA; they worked in the direction of a common conception: AI systems need to be more explainable to be better understood, trusted, and controlled. The main contribution of this DARPA XAI initiative is the creation of an XAI Toolkit, consolidating the diverse artifacts of the program (such as code, papers, reports, etc.) and the lessons learned from the 4-year program into a centralized publicly accessible repository ([[https://xaitk.org/](https://xaitk.org/)]([https://xaitk.org/](https://xaitk.org/)), accessed on 20 September 2023). This DARPA XAI program and the GDPR policy of the EU Parliament, along with the introduction of the EU AI Act (proposed the European law on Artificial Intelligence) ([[https://artificialintelligenceact.eu/](https://artificialintelligenceact.eu/)]([https://artificialintelligenceact.eu/](https://artificialintelligenceact.eu/)), accessed on 20 September 2023), contributed majorly to the explainable AI movement observed today [2]. The drive for greater transparency and accountability in AI is not limited to the global stage; it is also reflected in national initiatives, including those spearheaded by the New Zealand government. For example, various initiatives and intergovernmental standards have been adopted by the New Zealand government to address the transparency and accountability of AI algorithms. The G20 AI Principles, endorsed by leaders in June 2019 and based on the OECD AI Policy Observatory, serve as a framework to promote responsible AI use. Similarly, the 'Algorithm Charter for Aotearoa New Zealand' aligns with the OECD principles and aims to improve transparency and accountability in AI algorithm usage. The country also actively contributes to global efforts through the OECD portal ([[https://oecd.ai/en/dashboards/overview](https://oecd.ai/en/dashboards/overview)]([https://oecd.ai/en/dashboards/overview](https://oecd.ai/en/dashboards/overview)), accessed on 20 September 2023), which showcases AI policy initiatives worldwide and emphasizes the importance of AI transparency. The AI Forum NZ has released its own set of principles, including a focus on AI transparency, while other measures such as the New Zealand Pilot Project from the World Economic Forum ([[https://www3.weforum.org/docs/WEF_Reimaging_Regulation_Age_AI_2020.pdf](https://www3.weforum.org/docs/WEF_Reimaging_Regulation_Age_AI_2020.pdf)]([https://www3.weforum.org/docs/WEF_Reimaging_Regulation_Age_AI_2020.pdf](https://www3.weforum.org/docs/WEF_Reimaging_Regulation_Age_AI_2020.pdf)), accessed on 20 September 2023) further support the objective of improving AI transparency and explainability. Figure 1: Comparative analysis of black-box, gray-box, and white-box machine learning models, highlighting an apparent trade-off between accuracy (Y-axis) and interpretability (X-axis). After defining explainability and its significance, the subsequent text in this section explores the various objectives and approaches for XAI implementation, as outlined in the current literature. Following this, it delves into the taxonomy of XAI.