Robust Data-Driven Decision-Making: Human-AI Alignment, Adaptivity, and Optimality

Regents Of The University Of Michigan - Ann Arbor, Ann Arbor MI

Investigators

Abstract

AI-assisted decision-making systems have revolutionized fields ranging from medical treatment to online marketing by learning from data to optimize sequential decisions. However, real-world deployment reveals fundamental challenges that compromise system reliability and effectiveness. For instance, human users may distrust or selectively follow algorithmic recommendations, creating implementation gaps; operating environments often differ frequently from the conditions under which systems were trained; and complete knowledge about the environment is often unavailable. Existing methods typically assume perfect implementation and stable environments, leading to substantial performance degradation when these assumptions fail. This project aims to address these limitations by developing new theories and principled algorithms to enable robust and trustworthy decision-making under realistic constraints. In addition, it will provide valuable opportunities for training students at all levels in the STEM field and introducing the general audience to advances in data science and AI. This project focuses on fundamental sequential decision-making problems: multi-armed bandit and reinforcement learning. The research pursues three complementary directions, aiming to characterize the fundamental statistical limits of learning and develop provably optimal algorithms while maintaining robustness to varied sources of uncertainties. The first thrust will devise trust-aware procedures that account for human behavioral factors when individuals deviate from algorithmic recommendations. The second thrust will tackle the challenge of distribution shifts between training data and deployment environments through robust transfer learning methods. The third thrust aims to design model-agnostic algorithms that function across diverse model types and automatically adapt to unknown environmental structures. The project will provide statistical insights that inform decision-making practice and develop efficient, robust procedures for real-world applications across various domains. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.