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