Measuring artificial intelligence: a systematic assessment and implications for governance
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arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
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2022
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| _version_ | 1866912483191554048 |
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| author | Hötte, Kerstin Tarannum, Taheya Verendel, Vilhelm Bennett, Lauren |
| author_facet | Hötte, Kerstin Tarannum, Taheya Verendel, Vilhelm Bennett, Lauren |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2204_10304 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Measuring artificial intelligence: a systematic assessment and implications for governance Hötte, Kerstin Tarannum, Taheya Verendel, Vilhelm Bennett, Lauren General Economics Economics 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. |
| title | Measuring artificial intelligence: a systematic assessment and implications for governance |
| topic | General Economics Economics |
| url | https://arxiv.org/abs/2204.10304 |