A Conceptual Model of Trust in Generative AI Systems

Abstract

Generative Artificial Intelligence (GAI) significantly impacts various sectors, offering innovative solutions in consultation, self-education, and creativity. However, the trustworthiness of GAI outputs is questionable due to the absence of theoretical correctness guarantees and the opacity of Artificial Intelligence (AI) processes. These issues, compounded by potential biases and inaccuracies, pose challenges to GAI adoption. This paper delves into the trust dynamics in GAI, highlighting its unique capabilities to generate novel outputs and adapt over time, distinct from traditional AI. We introduce a model analyzing trust in GAI through user experience, operational capabilities, contextual factors, and task types. This work aims to enrich the theoretical discourse and practical approaches in GAI, setting a foundation for future research and applications.