Explainable AI Approach

Explainable AI (XAI) aims to make the decision-making processes of complex machine learning models transparent and understandable, addressing the "black box" problem. Current research focuses on developing and applying XAI methods across diverse fields, including healthcare, remote sensing, and finance, often employing techniques like SHAP values and concept backpropagation alongside model architectures such as random forests and gradient boosting machines. This work is crucial for building trust in AI systems, improving model interpretability for both developers and end-users, and facilitating the integration of AI into high-stakes applications where understanding the reasoning behind predictions is paramount.