提出轻量级AI安全开发流程，确保模型从生成到部署全程可验证。
Workflow for Safe-AI
- 基于扩展ONNX的流程，实现从训练到部署的全链路可验证
- 仅用少量精简工具，降低资源消耗并保障可靠性
- 适合高安全要求领域，如自动驾驶与医疗AI
安全可靠的AI模型在功能安全至关重要的应用中至关重要。面对AI研究快速进展与安全AI领域相对新颖的现状，亟需一种兼顾稳定与灵活的开发流程。本文提出一种透明、完整但轻量灵活的工作流，强调可靠性和可验证性。核心思想是流程本身必须具备可验证性，这要求使用经过认证的工具。工具认证成本高昂，因此我们重视轻量化设计，采用最少数量、功能有限的工具。该工作流基于扩展的ONNX模型描述，支持从算法生成到运行时部署的全流程验证，确保模型在不同运行环境，特别是混合关键性系统中可靠部署前已通过验证。
原文摘要 · Abstract (English)
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
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