The Value of Information in Human-AI Decision-making

Humans and AIs are often paired on decision tasks with the expectation of achieving complementary performance, where the combination of human and AI outperforms either one alone. However, how to improve performance of a human-AI team is often not clear without knowing more about what particular information and strategies each agent employs. We provide a decision-theoretic framework for characterizing the value of information -- and consequently, opportunities for agents to better exploit available information--in AI-assisted decision workflow. We demonstrate the use of the framework for model selection, empirical evaluation of human-AI performance, and explanation design. We propose a novel information-based instance-level explanation technique that adapts a conventional saliency-based explanation to explain information value in decision making.