Developing AI Safety-Critical Applications: A Framework Using Model-Based Design, Verification, and Validation

In the rapidly evolving landscape of Artificial Intelligence (AI), deploying AI models in safety-critical industries such as aerospace, automotive, and healthcare necessitates a rigorous framework for ensuring their accuracy, reliability, and trustworthiness. This paper delves into the crucial roles of Verification and Validation (V&V) techniques in the lifecycle of AI models, underlining their significance in the certification and deployment of AI systems in environments where errors can have severe consequences. Drawing upon recent advancements and regulatory frameworks, including adapting traditional V&V workflows into a more comprehensive W-shaped development process, the paper offers insights into the methodologies for systematically verifying and validating AI models. Through the lens of a case study on developing the Battery Heat Prediction Model, we exemplify the application of these V&V techniques from gathering requirements to creating a robust AI model. Through W cycles we focus and describe in detail the following different steps Requirements allocated to ML component management. Data Management Learning Process management Model Training Learning process verification Model Implementation Inference model verification & validation. Independent data and learning verification. ML requirements verification Special attention is given to the progress made in AI Model verification across safety-critical industries. we illustrate the iterative nature of V&V in AI, from initial requirements to achieving a robust model. Furthermore, the paper explores the significance of formal verification and explainability in reinforcing model robustness and fostering trust among end-users. Our discussion extends to the implications of these developments for the future of AI deployment in critical sectors, emphasizing the importance of V&V in building systems that are not only innovative but also safe, reliable, and trustworthy. This paper offers perspectives and methodologies that can aid stakeholders in navigating the complexities of AI verification and validation.