Explainable Bayesian Optimization
Tanmay Chakraborty Christin Seifert Christian Wirth
In industry, Bayesian optimization (BO) is widely applied in the human-AI
collaborative parameter tuning of cyber-physical systems. However, BO's
solutions may deviate from human experts' actual goal due to approximation
errors and simplified objectives, requiring subsequent tuning. The black-box
nature of BO limits the collaborative tuning process because the expert does
not trust the BO recommendations. Current explainable AI (XAI) methods are not
tailored for optimization and thus fall short of addressing this gap. To bridge
this gap, we propose TNTRules (TUNE-NOTUNE Rules), a post-hoc, rule-based
explainability method that produces high quality explanations through
multiobjective optimization. Our evaluation of benchmark optimization problems
and real-world hyperparameter optimization tasks demonstrates TNTRules'
superiority over state-of-the-art XAI methods in generating high quality
explanations. This work contributes to the intersection of BO and XAI,
providing interpretable optimization techniques for real-world applications.