文章基本信息

标题：Artificial Intelligence-Enhanced Decision Support for Informing Global Sustainable Development: A Human-Centric AI-Thinking Approach
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作者：Meng-Leong How ; Sin-Mei Cheah ; Yong-Jiet Chan 等
期刊名称：Information
电子版ISSN：2078-2489
出版年度：2020
卷号：11
期号：1
页码：39-62
DOI：10.3390/info11010039
出版社：MDPI Publishing
摘要：Sustainable development is crucial to humanity. Utilization of primary socio-environmental data for analysis is essential for informing decision making by policy makers about sustainability in development. Artificial intelligence (AI)-based approaches are useful for analyzing data. However, it was not easy for people who are not trained in computer science to use AI. The significance and novelty of this paper is that it shows how the use of AI can be democratized via a user-friendly human-centric probabilistic reasoning approach. Using this approach, analysts who are not computer scientists can also use AI to analyze sustainability-related EPI data. Further, this human-centric probabilistic reasoning approach can also be used as cognitive scaffolding to educe AI-Thinking in the analysts to ask more questions and provide decision making support to inform policy making in sustainable development. This paper uses the 2018 Environmental Performance Index (EPI) data from 180 countries which includes performance indicators covering environmental health and ecosystem vitality. AI-based predictive modeling techniques are applied on 2018 EPI data to reveal the hidden tensions between the two fundamental dimensions of sustainable development: (1) environmental health; which improves with economic growth and increasing affluence; and (2) ecosystem vitality, which worsens due to industrialization and urbanization.
关键词：artificial intelligence; decision making support; sustainability; environmental performance index; Bayesian; predictive modeling; human-centric; human-in-the-loop; AI-Thinking; explainable-AI; AI for good artificial intelligence ; decision making support ; sustainability ; environmental performance index ; Bayesian ; predictive modeling ; human-centric ; human-in-the-loop ; AI-Thinking ; explainable-AI ; AI for good