Over time, the concept evolved to be an immense growing research domain of explainable artificial intelligence (XAI). The bibliographic databases contain the earliest published evidence on the association between expert systems and the term explanation from the mid-eighties. Upon realising the need to explain AI/ML model-based intelligent systems, a few researchers started exploring and proposing methods long ago.

Thus, it has become a sine qua non to investigate how the inference mechanism or the decisions of AI/ML models can be made transparent to humans so that these intelligent systems can become more acceptable to users from different application domains. In general, almost all the prevailing expert systems built with AI/ML models do not provide additional information to support the inference mechanism, which makes systems nontransparent. In other words, the DL models function as a black-box. DL models’ outstanding performances hide one major drawback, i.e., the underlying inference mechanism remains unknown to a user. For example, today, deep learning (DL) models demonstrate convincing results with improved accuracy compared to established algorithms. In worse cases, users oppose the AI/ML model’s decision since their inference mechanisms are mostly opaque, unintuitive, and incomprehensible to humans. Users often remain doubtful about the proposed decisions. While these algorithms are meant to help users in their daily tasks, they still face acceptability issues. Different AI/ML algorithms are being employed to complement humans’ decisions in various tasks from diverse domains, such as education, construction, health care, news and entertainment, travel and hospitality, logistics, manufacturing, law enforcement, and finance. As a result, AI and ML are being used today in many application domains. With the recent developments of artificial intelligence (AI) and machine learning (ML) algorithms, people from various application domains have shown increasing interest in taking advantage of these algorithms. However, more attention is required to generate explanations for general users from sensitive domains such as finance and the judicial system. Research studies have been performed on the addition of explanations to widely used AI/ML models for expert users. This systematic synthesis of research articles resulted in several analytical findings: XAI methods are mostly developed for safety-critical domains worldwide, deep learning and ensemble models are being exploited more than other types of AI/ML models, visual explanations are more acceptable to end-users and robust evaluation metrics are being developed to assess the quality of explanations. This study considers 137 articles published in recent years and identified through the prominent bibliographic databases. Therefore, this paper presents a systematic literature review (SLR) on the recent developments of XAI methods and evaluation metrics concerning different application domains and tasks. Nonetheless, there is an evident scarcity of secondary studies in connection with the application domains and tasks, let alone review studies following prescribed guidelines, that can enable researchers’ understanding of the current trends in XAI, which could lead to future research for domain- and application-specific method development. The literature shows evidence from numerous studies on the philosophy and methodologies of XAI. To facilitate greater human acceptability of these systems, explainable artificial intelligence (XAI) has experienced significant growth over the last couple of years with the development of highly accurate models but with a paucity of explainability and interpretability. Artificial intelligence (AI) and machine learning (ML) have recently been radically improved and are now being employed in almost every application domain to develop automated or semi-automated systems.

Ifinance 4.2.3