Interpretable Optimization

Interpretable optimization aims to develop optimization methods that not only find optimal solutions but also provide clear explanations for those solutions, fostering trust and understanding. Current research focuses on integrating explainable AI techniques with various optimization algorithms, including Bayesian optimization and methods based on decision trees and geometric diffusion models, to enhance transparency and user confidence. This field is crucial for deploying optimization in high-stakes applications like drug discovery, material science, and industrial control systems where understanding the decision-making process is paramount for acceptance and responsible use. The ultimate goal is to bridge the gap between the power of sophisticated optimization techniques and the need for human interpretability and control.