Building trustworthy Artificial Intelligence through transparency explainability uncertainty and trust calibration

Artificial intelligence is now embedded in domains where reliability and accountability are critical, yet the opacity of many models continues to hinder user confidence. This paper argues that transparency and explainability are essential foundations of trust but must be situated within a broader epistemic context shaped by misinformation, bias, and human judgment under uncertainty. Drawing on recent scholarships and a systematic synthesis of evidence across healthcare, finance, autonomous vehicles, and e-commerce, we show that transparency and explainability can foster trust only when combined with mechanisms for uncertainty communication, trust calibration, and ethical safeguards. We propose the TEUT framework—Transparency, Explainability, Uncertainty, and Trust calibration—as a design principle that balances interpretability with performance and security, and we map it onto current governance initiatives such as the EU AI Act and the NIST AI Risk Management Framework. In doing so, the paper shifts the conversation from “opening the black box” to engineering accountable use, outlining a path for future research and policy that can make trust in AI measurable, auditable, and sustainable.