Transparent Machine Learning

Transparent machine learning (ML) aims to create models whose decision-making processes are readily understandable, enhancing trust and accountability. Current research emphasizes developing inherently interpretable models, such as additive models and prototype-based classifiers, and improving the efficiency of existing methods through optimized algorithms like those used in additive segmentation. This focus on transparency is crucial for building trust in high-stakes applications like healthcare and finance, where understanding model behavior is paramount for responsible deployment.