Dynamic Information Sub-Selection for Decision Support

Hung-Tien Huang, Maxwell Lennon, Shreyas Bhat Brahmavar, Sean Sylvia, Junier Oliva

Keywords: information selection, decision support, blackbox reward maximization

Abstract: We introduce Dynamic Information Sub-Selection (DISS), a novel framework of AI assistance designed to enhance the performance of black-box decision-makers by tailoring their information processing on a per-instance basis. Blackbox decision-makers (e.g. humans or real-time systems) often face challenges in processing all possible information at hand (e.g. due to cognitive biases or resource constraints), which can degrade decision efficacy. DISS addresses these challenges through a policy that dynamically select the most effective features and options to forward to the black-box decision-maker for prediction. We develop a scalable frequentist data acquisition strategy and a decision-maker mimicking technique for enhanced budget efficiency. We explore several impactful applications of DISS, including biased decision-maker support, expert assignment optimization, large language model decision support, and interpretability. Empirical validation of our proposed DISS methodology shows superior performance to state-of-the-art methods across various applications.

Primary Area: other topics in machine learning (i.e., none of the above)

Submission Number: 9667