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Socio-technical determinants of explainable artificial intelligence for infrastructure decision support.

Ahsan Waqar¹, Azlan Shah Ali², Khaled A Alrasheed³

¹Faculty of Built Environment, Universiti Malaya, Federal Territory of Kuala Lumpur, Malaysia. ahsanwaqar@um.edu.my.

Scientific Reports

|July 7, 2026

Summary

Trust in artificial intelligence (AI) for infrastructure management grows with transparency and accountability. Explainable AI (XAI) systems require socio-technical alignment for effective decision-making, not just digital readiness.

Keywords:

Algorithmic transparency Explainable artificial intelligence Perceived infrastructure decision quality Socio-technical systems Trust in AI

Related Experiment Videos

Area of Science:

Infrastructure Management
Artificial Intelligence
Explainable AI (XAI)

Background:

Growing AI use in infrastructure improves decision-making but faces trust issues due to algorithmic opacity.
Limited research exists on socio-technical factors for trustworthy explainable AI in infrastructure.
Existing studies focus on predictive performance, neglecting trust-building conditions.

Purpose of the Study:

Examine socio-technical determinants of trust in explainable AI.
Investigate factors influencing perceived infrastructure decision quality.
Understand how explainable AI impacts infrastructure management decisions.

Main Methods:

Quantitative research design.
Structured questionnaire survey with 283 infrastructure professionals.
Partial Least Squares Structural Equation Modeling (PLS-SEM) analysis.

Main Results:

Algorithmic transparency, perceived explainability, and decision accountability significantly enhance decision quality.
Transparency, explainability, and accountability strengthen trust in explainable AI.
Organizational digital readiness did not significantly impact trust in explainable AI.

Conclusions:

Effective AI-supported infrastructure decision-making hinges on aligning explainability with organizational governance.
Trust in explainable AI is built through transparency, explainability, and accountability.
Socio-technical factors are crucial for successful explainable AI adoption in infrastructure management.