What question did this study set out to answer?

The main aim is to establish how transparency and explainability in AI can cultivate user trust while addressing biases and misinformation.

April 15, 2026Discover Artificial IntelligenceOpen Access

Building trustworthy Artificial Intelligence through transparency explainability uncertainty and trust calibration

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The main aim is to establish how transparency and explainability in AI can cultivate user trust while addressing biases and misinformation.
Analyzed evidence from multiple domains including healthcare, finance, and e-commerce.
Proposed the TEUT framework that integrates transparency, explainability, uncertainty, and trust calibration.
Mapped the framework to existing governance initiatives like the EU AI Act.
Found that transparency and explainability enhance trust only when paired with uncertainty communication.
Highlighted the importance of ethical safeguards to ensure accountability in AI deployments.
Outlined a path for future research and policy to quantify trust in AI systems.

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Implication

Demonstrates how transparency, explainability, and uncertainty management foster trust in AI systems, suggesting essential frameworks for ethical accountability.

Cite This Study

Guo et al. (2026) studied this question.

synapsesocial.com/papers/69df2a99e4eeef8a2a6afa53 https://doi.org/10.1007/s44163-026-01219-x

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Implication

Demonstrates how transparency, explainability, and uncertainty management foster trust in AI systems, suggesting essential frameworks for ethical accountability.

Building trustworthy Artificial Intelligence through transparency explainability uncertainty and trust calibration

Key Points

Discussion

Implication

Cite This Study

Building trustworthy Artificial Intelligence through transparency explainability uncertainty and trust calibration

Key Points

Discussion

Implication

Cite This Study

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