Abstract
Explainable Artificial Intelligence (XAI) refers to the development of AI systems are transparent, explainable and their comprehensible for results can provide explanations or predictions. As AI technologies, particularly machine learning models, become more complex and sophisticated, there is a growing need to ensure that their decisions can be comprehended and trusted by humans, especially health, Finance and such as criminal justice in important domains. Evaluating Explainable Artificial Intelligence (XAI) is essential to ensure transparency, accountability, and user trust in AI systems. Interpretability is a key factor, examining how easily the model's internal mechanisms can be understood. Model transparency, feature importance, and the clarity of visualizations contribute to this aspect. Differentiate between post-hoc and intrinsic explanations, considering whether the model inherently provides interpretable insights. The distinction between local and global explanations is crucial, as it determines whether explanations focus on individual predictions or the overall model behavior. Robustness and consistency are assessed through stability and sensitivity analysis, ensuring that explanations remain reliable across similar instances. Additionally, ethical considerations, such as fairness and transparency in decision-making, must be addressed to uncover and mitigate biases. User feedback and the relevance of explanations to the specific use case contribute to a comprehensive evaluation, fostering the development of XAI systems that are not only technically robust but also ethically sound and user-friendly. The significance of research in Explainable Artificial Intelligence (XAI) lies in addressing critical challenges associated with the adoption and deployment of AI systems in various domains. As AI technologies, particularly complex machine learning models, become integral to decision-making processes in areas such as healthcare, finance, and criminal justice, the need for transparency and interpretability becomes paramount. Topsis involves optimizing from an advantageous standpoint by simultaneously minimizing the distance to and maximizing the distance from a reference point, which is defined in relation to solutions within a set of alternative options and numerous identification criteria. The importance of Topsis criteria lies in the potential to integrate comparative weights. This study conducts a comprehensive review of Topsis, exploring various weighing schemes and employing different distance measurements. Numerous applications of Topsis are examined, particularly its utilization in comparing results for a diverse set of multiple criteria data with varying weights. Interpretable Machine Learning Models, Human-Centric Design in XAI, Ethical Implications of XAI, Industry-specific Applications of XAI and Hybrid Approaches for Model Interpretability. Interpretability Metrics, Human-Subjective Evaluation, Algorithmic Robustness and Real-world Impact. the Ranking of Evaluation Explainable Artificial Intelligence. Industry-specific Applications of XAI is got the first rank whereas is the Ethical Implications of XAI is having the Lowest rank.