User trust in artificial intelligence: A comprehensive conceptual framework

Abstract

This paper provides a systematic literature review of current studies between January 2015 and January 2022 on user trust in artificial intelligence (AI) that has been conducted from different perspectives. Such a review and analysis leads to the identification of the various components, influencing factors, and outcomes of users' trust in AI. Based on the findings, a comprehensive conceptual framework is proposed for a better understanding of users' trust in AI. This framework can further be tested and validated in various contexts for enhancing our knowledge of users' trust in AI.This study also provides potential future research avenues. From a practical perspective, it helps AIsupported service providers comprehend the concept of user trust from different perspectives. The findings highlight the importance of building trust based on different facets to facilitate positive cognitive, affective, and behavioral changes among the users.

Developing trustworthy artificial intelligence: insights from research on interpersonal, human-automation, and human-AI trust

“…Similarly, Jing et al (2020) conducted a literature review and discovered a negative correlation between perceived uncertainties and risks with the acceptance of autonomous driving. Furthermore, perceived uncertainties in the application of AI vary across different applications, particularly pronounced in medical expert systems and vehicles ( Yang and Wibowo, 2022 ).…”

“…The invention of generative AI technologies, such as ChatGPT, marks a particularly significant leap in AI’s competence. While bringing significant changes to society, the development of AI has also sparked various concerns, including privacy invasion, hidden biases and discrimination, security risks, and ethical issues ( Yang and Wibowo, 2022 ). One response to these concerns is the emergence of and emphasis on trustworthy AI that aims to strike a good balance between technological advancement and societal and ethical considerations ( Li et al, 2023 ).…”

“…Overall, we aim to build a comprehensive framework for understanding and developing trustworthy AI that is grounded in end-users’ perspectives. Here, we focus primarily on the formation of trust, and do not distinguish between specific applications in automation or AI but refer to them collectively as automation or AI ( Yang and Wibowo, 2022 ). The following sections are organized according to the three types of trust, ending with a discussion on the implications of trust research on enhancing trustworthy AI.…”

Decoding Gen Z: AI's influence on brand trust and purchasing behavior

“…Research has shown that attitudes toward AI can significantly influence brand trust (Qin et al, 2020 ; Ahn et al, 2022 ; Yang and Wibowo, 2022 ). For instance, in the realm of e-commerce, studies have underscored the role of trust in online vendors and merchants in mitigating the perceived risks of online transactions.…”

Trust in artificial intelligence: From a Foundational Trust Framework to emerging research opportunities

“…The authors' framework is specific to AI. The Foundational Trust Framework can be used in conjunction with this paper to better understand the nature of trust in AI (Yang & Wibowo, 2022). • The paper "The effect of transparency and trust on intelligent system acceptance: evidence from a user-based study" by Jonas Wanner, Lukas-Valentin Herm, Kai Heinrich, and Christian Janiesch reports on a study related to trust in AI.…”