Explainable Preference Learning: Decision Trees Improve Bayesian Optimization

This research explores explainable preference learning, a critical area for understanding AI decision-making. The use of decision trees as a surrogate model for preferential Bayesian optimization offers a promising approach to enhance transparency and interpretability.

• •Investigates the use of decision tree-based surrogate models.
• •Focuses on improving the explainability of preference learning.
• •Applies findings to preferential Bayesian optimization.