Abstract
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.
MethodsWe 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.
Results35 studies involving diverse non-expert populations were included. We used an inductive thematic synthesis approach to identify 29 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.
ConclusionsThe 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.
This project has received funding from the European Union’s Horizon Europe research and innovation programme under grant agreement No 101136067(INVERSE).
The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
This work is licensed under a Creative Commons Attribution 4.0 International License.
Amendment
--Theoretical Framework (Section 2.2): We added explicit definitions to clarify the distinct roles and conceptual relationships among transparency, interpretability, and explainability within Human-AI Interaction. Furthermore, we incorporated a discussion on the technical requirement of validating XAI predictions and post-hoc explanations (e.g., using perturbation-based methods and fidelity assessments) to ensure explanations accurately reflect true model decision mechanisms rather than ungrounded statistical artifacts.
--Methodological process (Section 4): We expanded the methodological justification for employing a systematic snowballing review procedure to complement single-database searches and mitigate keyword limitations. In addition, we added a dedicated subsection (Section 4.3) detailing the step-wise inductive thematic synthesis procedure.
--Discussion (Section 6): We have completely revised the discussion section. We incorporated Table 7, which links trust factors with design considerations to interpret potential actionable strategies for XAI systems design and evaluation based on the literature. We also strengthened the connection between our proposed "forward-looking perspectives on AI trust and explainability" and the existing literature.
--Structural and Reference updates: Minor edits, updated citations, and structural refinements were implemented across all sections to enhance overall precision, logical flow, and manuscript readability.
