<p>Most of the high-performing artificial intelligence methods are black-box methods. Hence, they provide little insight regarding their decision logic, which hampers their application in critical domains like health care and security. This gave rise to the field of eXplainable Artificial Intelligence (XAI). XAI uses existing rule-based explainable methods, which are also self-explanatory, to emulate the decision-making process of a black-box method and thus provide insights into the workings of black-box methods. However, there are some drawbacks in the existing rules-based XAI model; amount of rules and specificity of rules are some of them. The available rules-based methods are not sufficient and efficient for providing both local and global explanations. In this article, we have shown how a rule-based method that is generated from the concept of partially defined Boolean function (pdBf), which is known as Logical Analysis of Data (LAD), can be used as an interpretable model. We have proposed two different algorithms for global and local explanation of any black-box classifier. GXALAD provides a global explanation of any model, while LXALAD provides all possible explanations of the prediction of any local instance. Our LAD-based method reduces rule generation complexity compared to state-of-the-art approaches and offers efficient performance with adaptable control parameters to address existing challenges.</p>