Complex Machine Learning

Complex machine learning focuses on developing and interpreting highly sophisticated models capable of handling intricate data patterns and making accurate predictions in diverse applications. Current research emphasizes improving model explainability through techniques like Shapley values and surrogate models (e.g., decision trees and GAMs), aiming to bridge the gap between predictive power and human understanding. This work is crucial for building trust in AI systems used in high-stakes decision-making, fostering collaboration between humans and machines, and ensuring fairness and ethical considerations are addressed in algorithmic design.