Trust is one of the most important factors shaping whether and how people adopt and rely on artificial intelligence (AI). Yet most existing studies measure trust in terms of functionality, focusing on whether a system is reliable, accurate, or easy to use, while giving less attention to the social and emotional dimensions that are increasingly relevant for today's generative AI (GenAI) systems. These systems do not just process information; they converse, respond, and collaborate with users, blurring the line between tool and partner. In this study, we introduce and validate the Human-AI Trust Scale (HAITS), a new measure designed to capture both the rational and relational aspects of trust in GenAI. Drawing on prior trust theories, qualitative interviews, and two waves of large-scale surveys in China and the United States, we used exploratory (n = 1,546) and confirmatory (n = 1,426) factor analyses to identify four key dimensions of trust: Affective Trust, Competence Trust, Benevolence & Integrity, and Perceived Risk. We then applied latent profile analysis to classify users into six distinct trust profiles, revealing meaningful differences in how affective-competence trust and trust-distrust frameworks coexist across individuals and cultures. Our findings offer a validated, culturally sensitive tool for measuring trust in GenAI and provide new insight into how trust evolves in human-AI interaction. By integrating instrumental and relational perspectives of trust, this work lays the foundation for more nuanced research and design of trustworthy AI systems.

However, a critical gap exists in the lack of generalizable and accurate specialized measurement tools Trust plays a crucial role not only in fostering cooperation, for assessing affective trust in the context of AI, especially efficiency, and productivity in human relationships (Brainov with the enhanced and nuanced capabilities of LLMs. This and Sandholm 1999) but also is essential for the effective highlights a need for a better measurement scale for affective use and acceptance of computing and automated systems, trust to gain a deeper understanding of how trust dynamics including computers (Madsen and Gregor 2000), automation function, particularly in the context of emotionally intelligent (Lee and See 2004), robots (Hancock et al. 2011), and AI. AI technologies (Kumar 2021), with a deficit in trust potentially In this paper, we introduce a 27-item semantic differential causing rejection of these technologies (Glikson and scale for assessing cognitive and affective trust in AI, Woolley 2020). The two-dimensional model of trust, encompassing aiding researchers and designers in understanding and improving both cognitive and affective dimensions proposed human-AI interactions. Our motivation and scale and studied in interpersonal relationship studies (McAllister development process is based on a long strand of prior research 1995; Johnson and Grayson 2005; Parayitam and Dooley on the cognitive-affective construct of trust that has 2009; Morrow Jr, Hansen, and Pearson 2004), have been shown to be important in interpersonal trust in organizations, been adopted in studying trust in human-computer interactions, human trust in conventional technology and automation, particularly with human-like technologies (Hu, Lu and more recently in trust towards AI.

A significant challenge to measuring human-automation trust is the amount of construct proliferation, models, and questionnaires with highly variable validation. However, all agree that trust is a crucial element of technological acceptance, continued usage, fluency, and teamwork. Herein, we synthesize a consensus model for trust in human-automation interaction by performing a meta-analysis of validated and reliable trust survey instruments. To accomplish this objective, this work identifies the most frequently cited and best-validated human-automation and human-robot trust questionnaires, as well as the most well-established factors, which form the dimensions and antecedents of such trust. To reduce both confusion and construct proliferation, we provide a detailed mapping of terminology between questionnaires. Furthermore, we perform a meta-analysis of the regression models that emerged from those experiments which used multi-factorial survey instruments. Based on this meta-analysis, we demonstrate a convergent experimentally validated model of human-automation trust. This convergent model establishes an integrated framework for future research. It identifies the current boundaries of trust measurement and where further investigation is necessary. We close by discussing choosing and designing an appropriate trust survey instrument. By comparing, mapping, and analyzing well-constructed trust survey instruments, a consensus structure of trust in human-automation interaction is identified. Doing so discloses a more complete basis for measuring trust emerges that is widely applicable. It integrates the academic idea of trust with the colloquial, common-sense one. Given the increasingly recognized importance of trust, especially in human-automation interaction, this work leaves us better positioned to understand and measure it.

Automated vehicles (AVs) are social robots that can potentially benefit our society. According to the existing literature, AV explanations can promote passengers' trust by reducing the uncertainty associated with the AV's reasoning and actions. However, the literature on AV explanations and trust has failed to consider how the type of trust - cognitive versus affective - might alter this relationship. Yet, the existing literature has shown that the implications associated with trust vary widely depending on whether it is cognitive or affective. To address this shortcoming and better understand the impacts of explanations on trust in AVs, we designed a study to investigate the effectiveness of explanations on both cognitive and affective trust. We expect these results to be of great significance in designing AV explanations to promote AV trust.