提出轻量可适配的AI分类器安全生成流程
An Early-Stage Workflow Proposal for the Generation of Safe and Dependable AI Classifiers
- 基于扩展ONNX模型定义透明可迭代的生成流程
- 流程兼顾安全性与对快速演进的AI领域的适应性
- 适合关注AI可信部署的研究者与工程团队
生成和执行可验证的安全可靠AI模型，需要一个透明、完整但又灵活且尽可能轻量的工作流程。鉴于人工智能研究的快速进展以及安全AI领域的相对不成熟，依赖功能安全发展的过程必须在稳定性与一定适应性之间取得平衡。本文提出一种早期工作流程，基于扩展的ONNX模型描述。一个使用案例为本研究提供了基础，我们期望未来能由第三方用例进一步扩展。
原文摘要 · Abstract (English)
The generation and execution of qualifiable safe and dependable AI models, necessitates definition of a transparent, complete yet adaptable and preferably lightweight workflow. Given the rapidly progressing domain of AI research and the relative immaturity of the safe-AI domain the process stability upon which functionally safety developments rest must be married with some degree of adaptability. This early-stage work proposes such a workflow basing it on a an extended ONNX model description. A use case provides one foundations of this body of work which we expect to be extended by other, third party use-cases.
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