Interpretable Machine Learning

Interpretable machine learning (IML) aims to develop machine learning models that are not only accurate but also transparent and understandable, addressing the "black box" problem of many high-performing models. Current research focuses on developing inherently interpretable models like generalized additive models (GAMs) and decision trees, as well as post-hoc methods that explain the predictions of complex models (e.g., using feature importance, Shapley values, or LLM-based explanations). This field is crucial for building trust in AI systems, particularly in high-stakes domains like healthcare and finance, where understanding model decisions is paramount for responsible and effective use.