Black Box Machine Learning Model

Black box machine learning models, characterized by their opaque internal workings, pose challenges for understanding their predictions and ensuring reliability. Current research focuses on developing methods to interpret these models, including techniques like feature importance analysis, counterfactual explanations, and local surrogate models (e.g., LIME, SHAP), aiming to improve transparency and trustworthiness. These efforts are crucial for building confidence in AI systems across diverse applications, from healthcare to finance, where understanding model decisions is paramount for responsible deployment and avoiding unintended consequences.