Decoding Trust in Artificial Intelligence: A Systematic Review of Quantitative Measures and Related Variables

As artificial intelligence (AI) becomes increasingly integral to our lives, ensuring these systems are trustworthy and transparent is paramount. The concept of explainability has emerged as a crucial element in fostering trust within AI systems. Nevertheless, the dynamics between explainability and trust in AI are intricate and not fully comprehended. This paper delves into the nexus between explainability and trust in AI, offering perspectives on crafting AI systems that users can rely on. Through an examination of existing literature, we investigate how transparency, accountability, and human oversight influence trust in AI systems and assess how various explainability approaches contribute to trust enhancement. Utilizing a set of experiments, our research examines how different explanatory models impact users' trust in AI systems, revealing that the nature and quality of explanations have a significant influence on trust levels. Additionally, we scrutinize the balance between explainability and accuracy in AI systems, discussing its implications for the development of reliable AI. This study underscores the critical role of explainability in engendering trust in AI systems, providing guidance on the development of AI systems that are both transparent and trustworthy, thereby fostering confidence among users.

The Meaning of “Explainability Fosters Trust in AI”

Artificial intelligence (AI) is increasingly integrated into healthcare decision-making. Public trust in AI remains a critical determinant of its acceptance and effective use. Evidence on the factors shaping trust in AI within Middle Eastern contexts, particularly Saudi Arabia, remains limited. Therefore, we aimed to identify the determinants of trust in AI for health-related decision-making and to examine a theory-informed mediation pathway in which patient satisfaction mediates the association between patient-doctor relationships and trust in AI. We conducted a cross-sectional, facility-based survey of adults in Saudi Arabia, using an electronic questionnaire distributed in four primary healthcare centers. We performed multiple linear regression to assess the association of trust in AI for health-related decision-making with patient satisfaction, patient-doctor relationships, sociodemographic characteristics, and healthcare-related factors. A mediation analysis was also employed to evaluate the indirect and direct association linking patient-doctor relationships, patient satisfaction, and trust in AI. Our findings showed that patient satisfaction was positively associated with trust in AI (β = 0.54, 95% CI: 0.18-0.90), while patient-doctor relationships showed an inverse association (β = -0.34, 95% CI: -0.48 to -0.20), possibly reflecting a greater reliance on physicians' clinical judgment and a reduced perceived need for AI-supported decision-making. Trust in AI varied across age groups, with a lower trust observed in older age categories compared with younger adults. No strong associations were observed for sex, education, body mass index, or healthcare-related factors. Patient-doctor relationship quality was indirectly associated with trust in AI via patient satisfaction (ACME = 0.138, 95% CI: 0.043-0.246), alongside a direct association with trust in AI (ADE = -0.313, 95% CI: -0.456 to -0.160). This means that patient-doctor relationships influenced trust in AI both directly and indirectly through patient satisfaction, suggesting that, while interpersonal care may reduce the reliance on AI (direct effect), enhancing patient satisfaction can partially offset this effect and promote trust in AI (indirect effect). These findings highlight that fostering patient-centered care and satisfaction may be crucial for promoting public trust in AI, which has important implications for AI governance, ethical deployment, and the design of AI-supported healthcare systems.

This systematic review examines how traditional Technology Acceptance Models (TAMs) can be adapted to better understand user acceptance of AI technologies. The study analyzed 80 peer-reviewed articles published between 2010 and 2025, identified through a comprehensive search of EBSCOhost, Embase, Inspec, Scopus, and Web of Science databases using keywords related to AI acceptance factors and technology acceptance models. Our analysis employed descriptive statistics, thematic analysis, and narrative synthesis to identify key factors influencing AI acceptance, including perceived usefulness, perceived ease of use, trust, transparency, explainability, and ethical considerations. We found that trust in AI systems' reliability, fairness, and privacy protection plays a crucial role in user acceptance, with low trust leading to resistance. Transparency and explainability of AI decision-making processes were identified as critical for building user trust. The review also highlights limitations of current research, including inconsistent definitions of AI technologies, inadequate exploration of cultural and contextual differences in AI acceptance, and the predominance of cross-sectional research designs. By addressing these areas, future research can provide a more comprehensive theoretical foundation for understanding user acceptance of AI technologies, guiding the development and ethical application of AI systems. Keywords: Artificial Intelligence (AI), Trust and Explainability in AI, Technology Acceptance Models (TAM).

Trust in artificial intelligence (AI) has emerged as a critical factor influencing students' interactions with AI-supported learning environments. However, validated instruments for measuring student trust in AI within educational contexts remain limited. This study developed and validated a multidimensional scale to assess student trust in AI systems in online distance education. Scale development followed a sequential multi-stage design, including item generation based on literature review, expert evaluation for content validity, and psychometric validation through exploratory and confirmatory factor analyses. A total of 837 distance learning students participated in the study (EFA = 412; CFA = 307; validation sample = 118). Exploratory factor analysis supported a five-factor structure explaining 63.20% of the total variance. Confirmatory factor analysis with an independent sample indicated good model fit ([Formula: see text]/df = 2.17; CFI = .95; TLI = .94; RMSEA = .06). The scale demonstrated high internal consistency (α = .94; ω = .94). Multi-group confirmatory factor analysis supported metric invariance across gender, indicating comparable measurement across groups. No significant gender differences were observed, and trust in AI was not significantly associated with grade point average or system engagement frequency. These results suggest that trust represents a distinct perception that may influence how students engage with AI-supported learning environments. The resulting 21-item scale provides a reliable instrument for investigating student trust in AI and offers practical implications for the design and implementation of AI-supported learning in higher education.

Artificial intelligence (AI) systems for decision-support are being introduced across multiple domains. One precondition for successful implementation is that those affected by the decisions trust the AI system. In the literature, many drivers of trust in AI have been proposed. In this study, we investigate the relative importance of six potential drivers of trust across three different decision-making domains, i.e., finance, legal, and healthcare in the Danish population. Based on an initial literature review and focus groups we designed a discrete choice study using conjoint analysis. Respondents were presented with a decision involving an AI system and asked to choose which of three possible AI systems they would trust the most, each system being characterized by a combination of levels of six attributes. All participants were presented with six choice situations. The decisions presented in the three domains were chosen to have approximately equal importance based on the focus group discussions. The three decisions were decision-making about a loan for a house purchase, decision-making about guilt in a case of assault, and diagnosis of skin cancer in primary care. Three samples of approximately 300 respondents each, representative of the Danish adult population were recruited through the panel provider Wilke. The main findings are that the most important drivers of trust differ between domains, with professional responsibility for the final decision being most important in finance and legal contexts, and precision being most important in the healthcare domain. Across all three domains, the absence of a particular trust-making feature has a strong negative effect, stronger than the presence of the same feature.

Artificial intelligence (AI) is rapidly integrating into infrastructure planning, design, construction, management, and operation. This encompasses the use of AI-powered, intelligent systems to process vast amounts of data and support human decision-making concerning urban development, architecture, transportation systems, housing, energy efficiency, and sustainability of the built environment. AI integration into the built environment has also introduced new challenges with respect to transparency, accountability, fairness, and reliability in machine decision-making, and led to concerns about algorithmic bias, privacy, and ethical deployment of technology. A key barrier to formalizing trust in AI lies in the absence of consistent definitions of the main constructs of trust, and their interplay in trust formation and calibration. Trust formation involves the initial establishment of confidence in AI systems, while calibration refers to the ongoing alignment of trust with system performance and user experiences. Understanding how these processes interact within a decision context is crucial for developing robust frameworks that enhance trust in AI. In the built environment domain, in addition to technical capabilities of the AI systems, it is also essential to consider social, ethical, and legal dimensions, to ensure that the outcomes serve the best interests of humanity. This paper provides an overview of trust in AI systems, followed by describing findings from the literature through the lens of several built environment decision-making scenarios with the goal of creating a common understanding to support future research on human trust in intelligent systems, and how it may influence the quality and timeliness of resulting decision outcomes.

Artificial intelligence (AI) is rapidly expanding in myriad industries and systems. This study sought to investigate public trust in using AI in the criminal court process. While previous research has identified factors that influence trust in AI, such as perceived accuracy and transparency of algorithms, less is known about the role of influential leaders—such as judges—in shaping public trust in new technology. This study examined the relationship between locus of control, anthropomorphism, cultural values, and perceived trust in AI. Participants completed a survey assessing their perceptions of trust in AI in determining bail eligibility, bail fines and fees, sentencing length, sentencing fines and fees, and writing legal documents (e.g., findings and disposition). Participants were more likely to trust AI performing financial calculations rather than determining bail eligibility, sentence length, or drafting legal documents. Participants’ comfort with AI in decision-making also depended on their perceptions of judges’ trust in AI, and they expressed concerns about AI perpetuating bias and the need for extensive testing to ensure accuracy. Interestingly, no significant association was found with other participant characteristics (e.g., locus of control, anthropomorphism, or cultural values). This study contributes to the literature by highlighting the role of judges as influential leaders in shaping public trust in AI and examining the influence of individual differences on trust in AI. The findings also help inform the development of recommended practices and ethical guidelines for the responsible use of AI in the courts.

The rapid evolution of artificial intelligence (AI) and cloud-based accounting systems (CBAS) presents transformative opportunities for small and medium enterprises (SMEs), yet adoption remains inconsistent in developing economies due to resource constraints, infrastructural gaps, and cultural skepticism. This study examines how AI practices influence intentions to adopt CBAS among SME accountants and owners in Southern districts Khyber Pakhtunkhwa (KP) Province, Pakistan, with a focus on the moderating role of trust in AI. Grounded in the Unified Theory of Acceptance and Use of Technology (UTAUT) and trust theory, the research proposes that AI practices (e.g., automation efficiency, predictive analytics) positively drive adoption intentions (H1), while trust in AI amplifies this relationship (H2). A cross-sectional survey of 401 SME professionals was conducted, utilizing validated scales to measure AI practices, trust in AI, and CBAS adoption intentions. Data analysis revealed a small but significant direct effect of AI practices on adoption intentions. Critically, trust in AI emerged as a significant moderator, strengthening the positive impact of AI practices on adoption decisions. This underscores trust’s role in mitigating perceived risks (e.g., data breaches, ethical concerns) and bridging the gap between technological utility and socio-cultural reservations in KP’s context. The findings extend UTAUT by integrating trust as a contextual moderator, offering a localized framework for AI adoption in high-stakes, resource-constrained environments. Practically, the study highlights the need for AI implementation strategies that prioritize transparency, ethical governance, and capacity-building initiatives tailored to SMEs’ socio-technical realities. Keywords: Artificial intelligence, cloud-based accounting, trust, SME adoption, UTAUT, Pakistan.

Artificial intelligence (AI) can provide many benefits in healthcare, including rapid and effective treatment options. However, previous research on human–computer interactions has demonstrated that people are reluctant to accept AI. This study compared individuals’ trust in AI with their trust in doctors regarding medical treatment and explored whether people trust an AI system that understands and suggests their desired treatments. We conducted an online experiment (N = 415) adopting a scenario method. The participants were asked to imagine receiving a medical examination and being prescribed medicine by a physician or an AI system. Participants trusted the AI system less than a doctor, even when the AI system learned and suggested their desired treatment. This finding confirms that people are reluctant to trust AI even if it performs at the level of a human doctor, thereby strengthening the existent literature that shows people prefer human doctors.

Background Trust in artificial intelligence (AI) systems is critical for ethical and effective adoption, particularly among non-expert users. This review aimed to identify factors influencing trust and practical considerations for designing explainable AI (XAI) systems. Methods We systematically reviewed the literature following PRISMA 2020 guidelines and using backward and forward citation searches. The primary search was carried out in Web of Science (WoS), with the final search completed in January 2025. Eligible studies were peer-reviewed publications examining trust in AI among non-expert users and/or design considerations for XAI. Exclusion criteria removed studies focused solely on expert users or unrelated to trust or explainability. Results 35 studies involving diverse non-expert populations were included. We used a thematic synthesis approach to identify 33 trust-related factors grouped into personal characteristics, sociocultural influences, design and interaction attributes, and system-level properties (e.g., age, emotions, digital literacy, social norms, usability, aesthetics, explanation type, accuracy, transparency, regulatory compliance). Additionally, 120 design considerations were extracted across nine domains, including type and form of explanation; user experience and interaction; trust, transparency and human oversight; robustness, safety and reliability; data, traceability and the AI lifecycle; regulation and compliance; ethics, values and social responsibility; evaluation methods and metrics; and usage context and organizational factors. Conclusions The review synthesizes emerging perspectives on AI trust and explainability and identifies six domains that warrant further research to support the responsible design and deployment of XAI systems. Limitations include heterogeneous evaluation practices and inconsistent operationalization of trust among non-expert users, which complicates comparability across studies and hinder the responsible deployment of XAI systems.

The rapid growth in adoption of artificial intelligence (AI) for decision-making processes, administrative functions and academic services has created an environment of rapid change throughout the university setting. As technology advances, concerns surrounding transparency, fairness and accountability of algorithms also arise; these issues affect how employees of universities perceive and trust AI systems. Research on AI governance and trust in organisations will be utilised in this study to explore how transparency and communication among leaders impact employee trust in AI as a governance tool within universities. Data from academic and administrative staff from universities that are currently undergoing transformation through digitalisation influenced by AI will be analysed using a structural equation modelling (SEM) method. The study will propose that algorithm transparency has both a direct and indirect positive correlation to employee trust through perceived organisational fairness. Additionally, the relationship between algorithm transparency and employee trust will be moderated by leader communication through reducing uncertainty and creating understanding of AI as a decision tool. Overall, this research is intended to advance the body of literature related to human-centred governance of AI by identifying socio-organisational mechanisms through which employee trust in AI systems develops. Furthermore, it will provide university leaders and policymakers with actionable recommendations for creating responsible governance structures for AI that prioritise transparency, communication and employee participation. By evaluating individual level trust related to the ethical implementation of AI at universities, our research will assist universities in understanding how they can balance their drive towards innovation through technology and caring for people.

Patient Perspectives on Artificial Intelligence in Radiology

The rise of Artificial Intelligence (AI) in our society is becoming ubiquitous and undoubtedly holds much promise. However, AI has also been implicated in high profile breaches of trust or ethical standards, and concerns have been raised over the use of AI in initiatives and technologies that could be inimical to society. Public trust and perceptions of AI trustworthiness underpin AI systems’ social licence to operate, and a myriad of company, industry, governmental and intergovernmental reports have set out principles for ethical and trustworthy AI. To guide the responsible stewardship of AI into our society, a firm foundation of research on trust in AI to enable evidence-based policy and practice is required. However, in order to inform and guide future research, it is imperative to first take stock and understand what is already know about human trust in AI. As such, we undertake a review of 100 papers examining the relationship between trust and AI. We found a fragmented, disjointed and siloed literature with an empirical emphasis on experimentation and surveys relating to specific AI technologies. While findings suggest some convergence on the importance of explainability as a determinant of trust in AI technologies, there are still gaps between conceptual arguments and what has been examined empirically. We urge future research to take a more holistic approach and investigate how trust in different referents impacts on attitudinal and behavioural intentions. Doing so will facilitate a more nuanced understanding of what it means to develop trustworthy AI.

Artificial intelligence (AI) has boundless potential in healthcare. In this chapter, we consider how to facilitate interactions between healthcare practitioners and AI systems. We discuss how AI can extend a practitioner’s expertise by leveraging the predictive power of AI systems. When AI systems are aligned with human values, practitioners can place appropriate trust in AI systems. Current applications of AI systems in clinical settings are limited to model-driven systems, some of which explicitly encode practitioner preferences as rules. We explore how AI systems can scrutinize datasets for insights, find new drugs or treatments, or provide clinical decision support to practitioners. Moreover, AI systems can show their trustworthiness to practitioners, by displaying predictability, procedural transparency, algorithmic transparency, and robustness. Practitioners are essential to the successful deployment of AI systems. The merit of AI in healthcare will come from AI deployed responsibly with practitioners in mind.