自动化生成符合标准的AI组件清单，助力合规与透明化管理
AIGen: Automating AI Bill of Materials Generation Through Hybrid MLOps Integration
- 基于MLflow框架融合启发式规则与大模型，自动提取AI系统组件
- 支持Hugging Face、PyTorch等多框架，可扩展插件接口
- 适配欧盟AI法案、NIST框架等，适用于企业级AI治理
负责任地开发和部署人工智能系统需要对其组成要素（如数据集、模型权重、训练管道和运行时依赖）进行严谨记录。尽管SPDX 3.0标准已原生支持AI和数据集描述，但能以自动化且可扩展方式生成符合标准的AI物料清单（AIBoM）的实用工具仍稀缺。本文提出AIGen，一个模块化AIBoM生成器，可产出符合SPDX 3.0 AI规范的机器可读、可互操作的AI系统组件清单。AIGen基于MLflow MLOps框架，结合挖掘启发式方法与大语言模型生成内容，并通过插件接口支持用户在不修改核心代码的前提下扩展领域特定采集器，兼容Hugging Face、PyTorch、TensorFlow等多种异构框架。该工具旨在促进对欧盟《人工智能法案》、NIST AI风险管理框架及ISO/IEC 42001的合规，为透明、可问责的AI供应链治理提供可复用的实践基础。
原文摘要 · Abstract (English)
The responsible development and deployment of artificial intelligence (AI) systems requires rigorous documentation of their constituent artifacts, e.g., datasets, model weights, training pipelines, and runtime dependencies. Although the Software Package Data Exchange (SPDX) 3.0 standard introduced native support for AI and dataset profiles, practical tooling capable of generating standards-compliant AI Bills of Materials (AIBoMs) in an automated and extensible manner remains scarce. This paper presents AIGen, a modular AIBoM generator that produces machine-readable, interoperable inventories of AI system components that comply with the SPDX 3.0 AI profile. AIGen works on top of the MLflow MLOps framework and combines mining heuristics with Large Language Models to generate AIBoMs. A plugin interface allows practitioners to extend the tool with domain-specific collectors without modifying the core codebase, supporting heterogeneous AI frameworks such as Hugging Face, PyTorch, and TensorFlow. AIGen is designed to facilitate compliance with the European Union AI Act, the NIST AI Risk Management Framework, and ISO/IEC 42001, providing a concrete, reusable foundation for transparent, accountable AI supply chain governance. Tool URL: https://github.com/danielebifolco/AIGen Tool Video: https://youtu.be/\_nAbXDWfVL4
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