| 任莎莎.基于PMC指数模型的北京市人工智能政策量化评价[J].全球科技经济瞭望,2021,36(10):54~62 |
| 基于PMC指数模型的北京市人工智能政策量化评价 |
| Quantitative Evaluation on Artificial Intelligence Policy in Beijing Based on PMC Index Model |
| 投稿时间：2021-08-22 |
| DOI：10.3772/j.issn.1009-8623.2021.10.009 |
| 中文关键词: 人工智能；PMC指数模型；政策评价；文本挖掘 |
| 英文关键词: artificial intelligence; PMC index model; policy evaluation; text mining |
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| 摘要点击次数: 1468 |
| 全文下载次数: 1993 |
| 中文摘要: |
| 本文对北京市人工智能政策进行量化评价，找出各项政策的优劣势并提出改进方向，为北京市人工智能政策的调整和完善提供参考。结合文本挖掘工具和北京市人工智能政策特点，本文构建了北京市人工智能政策PMC指数模型,通过政策的PMC指数得分情况，判断政策评价等级。对北京市5项人工智能政策进行量化评价的结果显示：五项政策中有一项为完美，三项为优秀，一项为可接受，说明北京市人工智能政策设计比较合理，但政策中仍存在一些不足：一是政策缺少建议性和诊断性；二是政策缺乏中长期发展目标；三是政策受体不够全面。最后，针对上述问题，本文提出对策建议。 |
| 英文摘要: |
| This article quantitatively evaluates the artificial intelligence policies in Beijing, identify compares the advantages and disadvantages of various policies, and proposes suggestions for improvement, and provides scientific reference for the adjustment and improvement of the artificial intelligence policies in Beijing. Combining text mining tools and the characteristics of artificial intelligence policy in Beijing, this study constructs a PMC index model for Beijing’s artificial intelligence policy, in which the policy can be evaluated through the PMC index score of the policy. This study quantitatively evaluates 5 artificial intelligence policies in Beijing. The evaluation results are:One of the five policies is perfect, three are excellent and one is acceptable.. Obviously, the overall design of Beijing’s artificial intelligence policy is relatively reasonable, but there are still some shortcomings: first, the policy lacks suggestive and diagnostic features; second, the policy lacks medium and long-term development goals; third, the policy recipients are not comprehensive. Finally, countermeasures and suggestions are put forward in response to the above problems. |
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