Interpretable Machine Learning Method

Interpretable machine learning (IML) aims to create machine learning models whose decision-making processes are transparent and understandable, addressing the "black box" problem of many complex models. Current research focuses on developing methods to explain feature importance, including interactions between features, and on evaluating the reliability and robustness of these explanations across various model architectures, such as decision trees, neural networks, and generalized additive models. This field is crucial for building trust in AI systems, particularly in high-stakes domains like healthcare and finance, enabling more reliable scientific discoveries and informed decision-making.