Looping in the Human Collaborative and Explainable Bayesian Optimization

TL;DR

This paper introduces CoExBO, a human-AI collaborative Bayesian optimization framework that integrates preference learning and explainability to enhance trust and performance in optimization tasks.

Contribution

CoExBO relaxes prior assumptions by incorporating human preferences without requiring explicit knowledge models, and provides explainability and no-harm guarantees.

Findings

CoExBO improves optimization performance in battery design.

Users gain better understanding through iterative explanations.

The framework maintains convergence even with adversarial user interventions.

Abstract

Like many optimizers, Bayesian optimization often falls short of gaining user trust due to opacity. While attempts have been made to develop human-centric optimizers, they typically assume user knowledge is well-specified and error-free, employing users mainly as supervisors of the optimization process. We relax these assumptions and propose a more balanced human-AI partnership with our Collaborative and Explainable Bayesian Optimization (CoExBO) framework. Instead of explicitly requiring a user to provide a knowledge model, CoExBO employs preference learning to seamlessly integrate human insights into the optimization, resulting in algorithmic suggestions that resonate with user preference. CoExBO explains its candidate selection every iteration to foster trust, empowering users with a clearer grasp of the optimization. Furthermore, CoExBO offers a no-harm guarantee, allowing users to make mistakes; even with extreme adversarial interventions, the algorithm converges asymptotically to a vanilla Bayesian optimization. We validate CoExBO's efficacy through human-AI teaming experiments in lithium-ion battery design, highlighting substantial improvements over conventional methods. Code is available https://github.com/ma921/CoExBO.