Many reliable decision support systems have been developed in recent years as “black boxes,” or systems that conceal their fundamental logic from the user. The necessity of Explainable Artificial Intelligence (XAI) has grown in order to better trust management and understand machine learning models. Thanks to this technology, the underlying data evidence and causal reasoning may now be comprehended by human professionals. This work's demonstration of the improvement in interpretability and social implications of post-hoc explanations of black-box ML models is one of the most significant developments in AI explainability. We describe three key results after testing our hypotheses with a representative consumer panel. We emphasize the need of using both expressible and evaluable behavioral markers in order to present a more complete picture of the interpretability aspects. Black-box model post-hoc explanations and explainable AI this research sheds fresh insight on how to improve privacy and trust. This comparison is expected to have a significant impact on how AI systems are developed and put into use in the future.