Introducing RUM: A Methodological Contribution for Engineering Trustworthy AI Components in Industrial Systems

Abstract

We introduce RUM, a unified, lifecycle-aware framework for facilitating the engineering and assessing Trustworthy AI Components—software units embedding both pure and statistical functions within real-world systems. Unlike model-centric evaluations, RUM treats AI Components as indivisible units whose behavior must be understood across specification, development, operation, and updating phases. This announcement paper presents a series of research articles that establish the foundation of RUM: (1) a formal argument for the atomic nature of Trustworthy AI Components; (2) a structured set of novel trust metrics, many of them being of non-aggregative nature, spanning the component's lifecycle; and (3) an operational framework introducing AI Blueprints to support runtime monitoring, human-in-the-loop usage, and temporal maintainability while facilitating the evolution of AI Components at different stages. RUM offers a coherent alternative to fragmented evaluation tools, aligning with the needs of AI deployment in industrial contexts.