High-risk AI systems—including autonomous vehicles, medical diagnostic platforms, financial risk engines, defense analytics, industrial robotics, and public-sector decision systems—require trustworthy user interfaces (UIs) that support transparency, reliability, interpretability, and safe oversight. Trustworthiness in these systems depends not only on algorithmic accuracy but also on effective communication of uncertainty, system intent, limitations, and confidence levels to human operators. This paper develops a conceptual framework for Trustworthy Interface Design for High-Risk AI Systems, grounded in theories of human–AI interaction, cognitive load, automation trust, and safety-critical system design. The framework consists of five pillars: interpretability, transparency, controllability, calibration of trust, and risk-aware feedback. Practical examples are drawn from aviation, healthcare, autonomous systems, and industrial automation. The paper concludes by outlining design guidelines, governance principles, and future directions for responsible AI deployment.