论文标题
衡量人工智能：系统评估和对治理的影响
Measuring artificial intelligence: a systematic assessment and implications for governance
论文作者
论文摘要
管理人工智能（AI）发明是一个主要的政策问题，但定义和衡量标准仍然有争议。我们比较了四种基于专利的方法，反映了对AI的不同理解。使用美国专利（1990-2019），我们评估了每种方法将AI分类为通用技术（GPT）的程度，并检查专利浓度 - 两个与中央策略相关的维度。这些方法仅在1.37％的专利中重叠，在2019年将美国专利的3-17％定义为AI。所有方法都证实了AI的GPT特性，基于最小的基于关键字的集合表现出最高的增长和一般性。高GPTNESS表示公共良好特征，证明公共支持是合理的。在方法中，AI专利集中于少数公司，强调了市场能力和监管挑战。因此，政策实施需要仔细考虑多个分类方法，以确保稳健，包容和有效的AI治理。
Governing artificial intelligence (AI) inventions is a major policy concern, yet definitions and measurement remain contested. We compare four patent-based approaches reflecting distinct understandings of AI. Using US patents (1990-2019), we assess the degree to which each approach classifies AI as a general-purpose technology (GPT) and examine patent concentration--two central policy-relevant dimensions. The approaches overlap in just 1.37% of patents, defining between 3-17% of all US patents in 2019 as AI. All approaches confirm AI's GPT characteristics, with the smallest keyword-based set exhibiting the highest growth and generality. High GPTness indicates public good characteristics, justifying public support. Across methods, AI patents concentrate among a few firms, highlighting market power and regulatory challenges. Policy implementation, thus, requires careful consideration of multiple classification methods to ensure robust, inclusive, and effective AI governance.
