揭示政府人工智能就绪指数的隐藏内涵:模糊 LMAW 和 Schweizer-Sklar 加权框架的应用

M. K. Nasution, M. Elveny, D. Pamučar, Milena Popovic, Bisera Andrić Gušavac
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摘要

人工智能(AI)和算法大有可为,世界各国政府越来越多地投资于这一变革性技术。其潜在好处包括提高绩效、降低成本、高效管理、预测和预防犯罪等。人工智能时代有望彻底改变社会的各个方面。然而,随着各国准备利用人工智能的力量,关于政府应用人工智能准备程度的排名的有效性也出现了问题。本文对《牛津洞察》人工智能就绪指数的权重标准进行了分析,旨在提供更准确的评估。本文采用算术和几何非线性函数来分析和评估各国的排名,而不是传统的平均值。通过聚类分析,根据观察到的标准将国家分为三个不同的组别,从而从细微处透视政府的人工智能就绪程度。这种聚类方法不仅有助于根据各国的人工智能准备程度对其进行更有效的分类,还能突出每个聚类中的差异和相似性,从而更深入地了解区域趋势,并在每个聚类中准确定位有针对性的改进战略。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Uncovering the Hidden Insights of the Government AI Readiness Index: Application of Fuzzy LMAW and Schweizer-Sklar Weighted Framework
There is considerable promising in artificial intelligence (AI) and algorithms, with governments worldwide increasingly investing in this transformative technology. The potential benefits include improved performance, cost reduction, efficient management, and crime prediction and prevention, among others. The AI era holds the promise of revolutionizing various aspects of society. However, as countries prepare to leverage the power of artificial intelligence, questions arise about the validity of rankings published on the readiness of the governments for the application of AI. In this article, the weighting criteria that are analysed in the Oxford Insights AI Readiness Index are scrutinized, aiming to provide a more accurate assessment. Instead of conventional averaging, arithmetic and geometric non-linear functions are employed to analyse and assess the rank of the countries. Through clustering analysis, countries are categorized into three distinct groups based on observed criteria, offering a nuanced perspective on government AI readiness. This clustering approach not only facilitates a more effective categorization of countries based on their AI preparedness, but also accentuates the variations and similarities within each cluster, which enables deeper insights into regional trends and pinpoint targeted strategies for enhancement within each cluster.
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