RAIL: An Automatic Classifier of the Artificial Intelligence Readiness Level· RAIL：自动分类AI技术成熟度
Assessing the maturity of artificial intelligence technologies is essential for investment decisions, project management, and policy monitoring, yet the available readiness frameworks are heterogeneous and difficult to apply automatically: the adaptation of Technology Readiness Levels to AI lacks AI-specific gating criteria, the Machine Learning Technology Readiness Levels presuppose access to internal process artifacts, and AI/data readiness dimension models employ scales that resist direct comparison. This paper makes two contributions. First, we unify these three frameworks into the Unified AI Readiness Level (AIRL), a nine-level ordinal scale built on an environmental evidence ladder and complemented by dimensional caps (covering specification, data existence, data quality, data legali
提出统一AI技术成熟度评估框架，实现自动分类。
- 核心方法
- 整合三种现有框架，构建基于环境证据梯度的九级统一AI成熟度量表，并通过维度上限进行补充。
- 适合谁读
- 研究者、工程师、政策制定者
- 要解决的问题
- 现有AI技术成熟度评估框架异质性强、难以自动应用。
- 关键实验
- 未提供
- 主要贡献
- 1. 统一多种成熟度评估框架；2. 实现AI技术成熟度的自动分类；3. 提供更具体、可比的评估标准。
- 意义与局限
- 有助于投资决策、项目管理和政策监控，但评估标准的通用性仍需进一步验证。