ARCHIVES

Explainable Artificial Intelligence (XAI) has become an important area of work in overcoming the shortcomings of the conventional models of deep learning which tend to act as black boxes. Although such models are highly predictive, they are not interpretable, which raises questions about their reliability and responsibility and ethical use in sensitive areas like healthcare and finance. The presented work is aimed at enhancing the level of transparency of deep learning models with the help of such sophisticated XAI methods as SHAP ( SHapley Additive exPlanations ) and LIME (Local Interpretable Model-agnostic Explanations ). These techniques can be used to interpret model predictions, determining the impact of input features and give human-interpretable explanations. The introduced solution combines explainability functionality into the machine learning pipeline to make the process of decision-making more transparent without impacting the model performance to a considerable extent. With SHAP to analyze feature importance globally and locally and LIME to analyze the behavior of the model on a case-by-case basis, the system has insights into the behavior of the models. Experimental results show that using XAI methods can enhance user trust, debugging of the model, and adherence to ethical and regulatory requirements. These findings demonstrate the need to provide explainability in the implementation of responsible and trustworthy AI systems in the real world.