Modelling the quantum-like dynamics of human reliability ratings in human-AI interactions by interaction-dependent...

We are rapidly moving to a future in which our information environment is saturated by artificial intelligence (AI), and humans and AI agents will routinely engage in shared decision making even in conditions of high uncertainty and risk (such as natural disasters or nuclear accidents). Trust is fundamental to the effectiveness of these interactions. A key challenge in modelling the dynamics of trust in human-AI interactions is to provide a means to integrate the diversity of human trust fluctuations found empirically. In this article, we explore the ability of quantum random walk (QRW) models to model this dynamism of trust found in empirical human-AI interactions. Specifically, we manipulate certain features of the QRW to explore its ability to provide the necessary agility and sensitivity to fluctuations in trust judgments. The goal is to incorporate the nature of the interaction itself into the evolution of the model, with the features stemming from empirically derived parameters. We found that using empirical parameters to inform the use of different Hamiltonians throughout the interaction markedly influences the modelled trust dynamics and can provide a promising means to model the evolution of trust in human-AI interactions.This article is part of the theme issue 'Quantum theory and topology in models of decision making (Part 1)'.