Interpretable Machine

Interpretable machine learning (IML) aims to create machine learning models whose decision-making processes are transparent and understandable to humans, addressing the "black box" problem of many complex models. Current research focuses on developing inherently interpretable models like rule-based systems and tree ensembles, as well as employing post-hoc explanation techniques to analyze existing models, often using methods like SHAP values or feature importance analysis. This field is crucial for building trust in AI systems across diverse applications, from healthcare (e.g., disease prognosis, risk prediction) and environmental science (e.g., climate change impact assessment) to engineering (e.g., material optimization) and legal domains, where understanding the reasoning behind predictions is paramount for responsible and effective use.