Assurance of Safety-Critical AI-Enabled Capabilities: Integrating Human, Software, and Machine Learning Assurance Paradigms

Artificial Intelligence (AI)-enabled systems are now increasingly deployed across safety-critical, mission-critical, and socio-technical domains. Existing engineering disciplines offer mature approaches to human and software assurance; however, AI-enabled systems can exhibit characteristics such as probabilistic behaviour, data dependency, limited explainability, and adaptation that challenge traditional assurance methods. Our research examines assurance precepts across three domains that arguably dominated assurance reforms in capability development during different periods: (1) human assurance, circa 1980 to 2000, (2) software assurance, circa 2000 to 2020, and (3) assurance of AI-enabled systems, emerging since 2020. While software assurance is largely concerned with verifying deterministic behaviour against specified requirements and human assurance focuses on managing variability in socio-technical systems, AI assurance must address systems whose behaviour is learned, probabilistic, and inherently dependent on data. We contribute a set of 20 assurance precepts for safety-critical AI-enabled capabilities. To extend current assurance frameworks, we structured these precepts across four quadrants of assurance activities, responsibilities, and critical questions, and organized them within a novel Dual Assurance Spiral for AI-enabled Capabilities (DAS4AIC). We propose the DAS4AIC to operationalise the 20 AI assurance precepts throughout capability development and sustainment. Further, we demonstrate how classical safety precepts map from human-dominant assurance through software-dominant assurance to the assurance of AI-enabled systems with conceptual validation. Our preliminary evaluation is that some human assurance precepts map more directly to AI-enabled systems than through the intermediate step of software assurance, suggesting that established human assurance concepts may have continuing relevance for AI-enabled capabilities. To our knowledge, this is the first study to align emerging AI-assurance precepts explicitly with the earlier and still overlapping, human-dominant and software-dominant assurance paradigms.