# Explainable Artificial Intelligence: Advancements and Limitations
## 1 Introduction  
Deep neural networks (DNNs) have demonstrated impressive predictive performance across various domains, including medicine [1, 2], robotics [3, 4] and economics [5]. They have been successfully applied to a wide range of problems, such as object detection [6, 7], stock prediction [8], image generation [9, 10], and machine translation [11, 12], among many others. This success can be attributed to advancements in deep learning [13], the availability of massive datasets [14], and the increasing computational power provided by graphics processing units (GPUs) [15]. Despite the significant performance improvements in DNNs over recent years, gaining trust in their predictions remains a challenge due to the complex and opaque nature of their decision-making processes [16, 17, 18, 19, 20, 21]. To address this, model interpretability has become essential in uncovering the \"black box\" of deep networks. Interpretability, often defined as _the ability to provide explanations in terms understandable to humans_[22], plays a crucial role in bridging the gap between DNNs' performance and user trust. Ongoing discussions have explored the distinctions between terms like interpretability, explainability, trustworthiness, and transparency, debating their appropriate use in the field [18, 23, 24, 25]. In this review, we focus on the shared goal of explainable artificial intelligence (XAI) methodologies--to make AI more understandable to humans--and leave a detailed discussion of the differences among these approaches for future work. The techniques developed to interpret and explain machine learning (ML) models are broadly categorised as XAI methodologies [26, 27]. Consequently, this review adopts XAI as its core term. In recent years, the field of XAI has experienced exponential growth,with numerous approaches proposed to address the transparency challenge of black-box models. These approaches aim to make such models safer for deployment, especially in sensitive domains, including healthcare [28; 29] and law [30]. By improving transparency, XAI seeks to achieve several objectives, such as ensuring fairness by detecting and mitigating discrimination or unexpected behaviours in ML systems. XAI techniques also assist practitioners in debugging their systems by identifying biases in data or in the models themselves [20; 31]. Furthermore, explainability is not merely a desirable feature; since 2018, the General Data Protection Regulation (GDPR) in Europe has mandated that artificial intelligence (AI) systems provide justifications for their decisions, further underscoring the critical role of XAI [32; 33]. There have been a significant number of reviews on XAI focusing on different aspects of this important subfield. These reviews share many common goals, such as the definition of interpretability and its types [16; 17]. Some of these reviews specifically focus on the detailed taxonomy of existing methodologies and providing the differences between terms like explainability, interpretability and transparency [18; 34]. Some surveys, on the other hand, highlight the importance of explainability in modern technologies, particularly within the application domains of Industry 4.0 [35]. Our main contribution is, however, the definition of the dimensions of XAI, which, to the best of our knowledge, has never been explicitly put together before. We define six main dimensions, where each of them is further divided into two subcategories. We claim that any XAI methodology currently exists or proposed in the future could be put under these categories. Proposed techniques frequently belong to multiple subcategories within these dimensions. For instance, a single method might generate both visual and conceptual explanations or be applicable to both traditional ML models and deep learning architectures. This overlapping nature reflects the versatility and multifaceted nature of XAI techniques, which are not confined to a single category but instead span across several, depending on their design and application.