ACM Computing Surveys / 2026

Building Trust in Artificial Intelligence: A Systematic Review through the Lens of Trust Theory

Massimo Regona, Tan Yigitcanlar, Carol K. H. Hon, Melissa Teo

AI SafetyFoundation ModelsPopular and Landmark PapersRobotics

Artificial intelligence (AI) is reshaping industries by enhancing efficiency and accuracy, yet its adoption remains contingent on user trust, which is frequently undermined by concerns over privacy, algorithmic bias, and security vulnerabilities. Trust in AI depends on principles such as transparency, accountability, safety, privacy, robustness, and reliability, all of which are central to user confidence. However, existing studies often overlook the interdependencies among these factors and their collective influence on user engagement. Guided by Trust Theory and a systematic literature review employing the PRISMA protocol, this study examines the trust indicators most relevant to high-stakes applications. The review reveals that transparency and communication are consistently prioritised, while adaptability and affordability remain underexplored, highlighting gaps in current scholarship. Trust in AI evolves as users gain experience with these systems, with reliability, predictability, and ethical alignment emerging as critical determinants. Addressing persistent challenges such as bias, data protection, and fairness is essential for reinforcing trust and enabling broader adoption of AI across industries.

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Title: Building Trust in Artificial Intelligence: A Systematic Review through the Lens of Trust Theory
Authors: Massimo Regona, Tan Yigitcanlar, Carol K. H. Hon, Melissa Teo
Year: 2026
Venue: ACM Computing Surveys
Categories: AI Safety, Foundation Models, Popular and Landmark Papers, Robotics
Citations: 3
Paper URL: https://www.semanticscholar.org/paper/15001725119ad7dd0da3e47be1fe16dbdc7d175f
Open PDF: Not available

Abstract:
Artificial intelligence (AI) is reshaping industries by enhancing efficiency and accuracy, yet its adoption remains contingent on user trust, which is frequently undermined by concerns over privacy, algorithmic bias, and security vulnerabilities. Trust in AI depends on principles such as transparency, accountability, safety, privacy, robustness, and reliability, all of which are central to user confidence. However, existing studies often overlook the interdependencies among these factors and their collective influence on user engagement. Guided by Trust Theory and a systematic literature review employing the PRISMA protocol, this study examines the trust indicators most relevant to high-stakes applications. The review reveals that transparency and communication are consistently prioritised, while adaptability and affordability remain underexplored, highlighting gaps in current scholarship. Trust in AI evolves as users gain experience with these systems, with reliability, predictability, and ethical alignment emerging as critical determinants. Addressing persistent challenges such as bias, data protection, and fairness is essential for reinforcing trust and enabling broader adoption of AI across industries.

Learning resources:
- Google Scholar: Google Scholar references (https://scholar.google.com/scholar?q=Building%20Trust%20in%20Artificial%20Intelligence%3A%20A%20Systematic%20Review%20through%20the%20Lens%20of%20Trust%20Theory)
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- Semantic Scholar: Semantic Scholar paper page (https://www.semanticscholar.org/paper/15001725119ad7dd0da3e47be1fe16dbdc7d175f)
- YouTube: YouTube explanations (https://www.youtube.com/results?search_query=Building%20Trust%20in%20Artificial%20Intelligence%3A%20A%20Systematic%20Review%20through%20the%20Lens%20of%20Trust%20Theory+paper+explained)