Workflow for Safe-AI
It addresses the problem of ensuring functional safety in AI deployment for mixed-criticality systems, but appears incremental as it builds on existing tools and standards.
The paper tackles the need for a workflow to develop safe and dependable AI models in safety-critical applications by proposing a transparent, lightweight workflow that emphasizes reliability and qualifiability, using an extended ONNX model description for validation from generation to deployment.
The development and deployment of safe and dependable AI models is crucial in applications where functional safety is a key concern. Given the rapid advancement in AI research and the relative novelty of the safe-AI domain, there is an increasing need for a workflow that balances stability with adaptability. This work proposes a transparent, complete, yet flexible and lightweight workflow that highlights both reliability and qualifiability. The core idea is that the workflow must be qualifiable, which demands the use of qualified tools. Tool qualification is a resource-intensive process, both in terms of time and cost. We therefore place value on a lightweight workflow featuring a minimal number of tools with limited features. The workflow is built upon an extended ONNX model description allowing for validation of AI algorithms from their generation to runtime deployment. This validation is essential to ensure that models are validated before being reliably deployed across different runtimes, particularly in mixed-criticality systems. Keywords-AI workflows, safe-AI, dependable-AI, functional safety, v-model development