2022/04/21 by Kerstin Hötte, Taheya Tarannum, Hötte, Kerstin +5
Decision Sciences · Economics, Econometrics and Finance · #Energy, Environment, Economic Growth #FOS: Economics and business #General Economics (econ.GN) #Innovation Diffusion and Forecasting #Innovation Policy and R&D
paper · pdf · doi:10.48550/arxiv.2204.10304
openalex publication_date 2022/04/21 · openalex created_date 2022/04/26 · openalex updated_date 2026/07/28
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.