A Quantum Probability Approach to Improving Human-AI Decision-Making

Source abstract

Artificial Intelligence (AI) is set to incorporate expanded decision space that has traditionally been the purview of humans. However, AI systems that support decision-making also entail human rationalization of AI outputs. Yet, incongruencies between AI and human rationalization processes may introduce uncertainties into human decision-making, necessitating new conceptualizations to improve the predictability of these interactions. The application of quantum probability theory (QPT) to human cognition is on the ascent and warrants potential consideration in human-AI decision-making to improve outcomes. This perspective paper explores how QPT may be applied to human-AI interactions and contributes by integrating these concepts into human-in-the-loop decision-making. To capture this and offer a more comprehensive conceptualization, we use human-in-the-loop constructs to explicate how recent applications of QPT can ameliorate the models of interaction by providing a novel way to capture emergent behaviors. Followed by a summary of the challenges posed by human-in-the-loop systems, we discuss emerging theories that advance models of the cognitive system by using quantum probability formalisms. We conclude by outlining areas of promising future research in human-AI decision-making in which proposed methods may apply.