AI-DRIVEN INTERFACES

This study investigates how transparency and explainability influence user trust in AI-driven interfaces. As AI systems become increasingly embedded in decision-making, users often struggle to understand their processes, leading to skepticism and reduced adoption. Using a mixed-methods approach, data were collected from 62 participants through structured questionnaires assessing transparency, explainability, trust, and user experience. Statistical analyses revealed that higher transparency and clear, human-centered explanations significantly enhance user trust and perceived fairness. However, overly technical or complex disclosures reduce comprehension and engagement. The study proposes a Human-Centered AI framework that integrates adaptive explainability, layered transparency, and user feedback mechanisms. Findings contribute to the growing field of trustworthy AI by offering practical guidelines for designing transparent, ethical, and user-aligned AI interfaces.