Year: 2026
Type: article
Abstract: Trust is a multi-faceted phenomenon traditionally studied in human relations and more recently in human-machine interactions. In the context of AI-enabled systems, trust is about the belief of the user that in a given scenario the system is going to be helpful and safe. The system-side counterpart to trust is trustworthiness. When trust and trustworthiness are aligned with each other, there is calibrated trust. Trust, trustworthiness, and calibrated trust are all dynamic phenomena, evolving throughout the history and evolution of user beliefs, systems, and their interaction. In this paper, we review the basic concepts of trust, trustworthiness and calibrated trust and provide definitions for them. We discuss their various metrics used in the literature, and the causes that may affect their dynamics, particularly in the context of AI-enabled systems. We discuss the implications of the discussed concepts for various types of stakeholders and suggest some challenges for future research.
Language: English
Subfield: Social Psychology
Field: Psychology
Domain: Social Sciences
Open Access status: hybrid
Estimated APC paid: $3,290
Funder: Universität Duisburg-Essen
OpenAlex ID: W7128797970