A Conceptual Framework for the SE of AI‐Intensive Systems (SE4AI) – Considering Data Through the Life‐Cycle

Abstract

AbstractThis paper considers the challenge of the Systems Engineering of AI‐Intensive Systems (SE4AI) with a particular focus on their high reliance on data for model design verification, model training and model validation. The paper opens by describing the contextual background and reviewing contemporary AI/ML development approaches, identifying these to be largely software based “bottom‐up” driven by technological advancements, lacking a holistic top‐down “end‐to‐end” systems‐based perspective. Concepts and principles from systems thinking, modelling and simulation, and situation awareness are then briefly described in part two, as exaptive enabling building blocks, for integration into best‐practice evolutionary SE life‐cycle processes. A proposed tailored evolutionary SE life‐cycle process, with a focus on model design verification, model training and model validation data is then described in part three, through an example life‐cycle iteration, through each phase, in terms of the phase objective, the proposed refinement to the phase activities to cater for ML systems and the proposed phase outcomes. Related initiatives of direct relevance to this research are identified in part four, followed by a summary and conclusion.