Artificial Intelligence-Enhanced Decision Support for Informing Global Sustainable Development: A Human-Centric AI-Thinking Approach

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.

[1]  Tao Chen,et al.  Response surface methodology with prediction uncertainty: A multi-objective optimisation approach , 2012 .

[2]  W. D. Hung,et al.  Harnessing Entropy via Predictive Analytics to Optimize Outcomes in the Pedagogical System: An Artificial Intelligence-Based Bayesian Networks Approach , 2019, Education Sciences.

[3]  Abdul Aziz Jemain,et al.  Bayesian structural equation modeling for the health index , 2013 .

[4]  T. Aishan,et al.  Model Prediction of Secondary Soil Salinization in the Keriya Oasis, Northwest China , 2018 .

[5]  Erol Gelenbe,et al.  A Sustainable Model for Integrating Current Topics in Machine Learning Research Into the Undergraduate Curriculum , 2009, IEEE Transactions on Education.

[6]  Lakhmi C. Jain,et al.  Introduction to Bayesian Networks , 2008 .

[7]  David Elgart,et al.  Bayesian credible intervals for response surface optima , 2009 .

[8]  A. Hsu,et al.  What progress have we made since Rio? Results from the 2012 Environmental Performance Index (EPI) and Pilot Trend EPI , 2013 .

[9]  Zhenzhong Xu,et al.  A Bayesian Failure Prediction Network Based on Text Sequence Mining and Clustering , 2018, Entropy.

[10]  Lauri Ojala,et al.  Connecting to Compete 2018 , 2018 .

[11]  John J. Peterson,et al.  A Bayesian Approach for Multiple Response Surface Optimization in the Presence of Noise Variables , 2004 .

[12]  Beata Beigman Klebanov,et al.  Reflective Writing About the Utility Value of Science as a Tool for Increasing STEM Motivation and Retention – Can AI Help Scale Up? , 2017, International Journal of Artificial Intelligence in Education.

[13]  W. Ziemba,et al.  The Swiss Black Swan Bad Scenario: Is Switzerland Another Casualty of the Eurozone Crisis? , 2015 .

[14]  Brian A. Nosek,et al.  Power failure: why small sample size undermines the reliability of neuroscience , 2013, Nature Reviews Neuroscience.

[15]  Douglas C. Montgomery,et al.  Response Surface Methodology: Process and Product Optimization Using Designed Experiments , 1995 .

[16]  Joop J. Hox,et al.  How few countries will do? Comparative survey analysis from a Bayesian perspective , 2012 .

[17]  Pedro M. Domingos,et al.  On the Optimality of the Simple Bayesian Classifier under Zero-One Loss , 1997, Machine Learning.

[18]  Claude E. Shannon,et al.  The lattice theory of information , 1953, Trans. IRE Prof. Group Inf. Theory.

[19]  Andreas Holzinger,et al.  From Machine Learning to Explainable AI , 2018, 2018 World Symposium on Digital Intelligence for Systems and Machines (DISA).

[20]  Meng-Leong How,et al.  Educing AI-Thinking in Science, Technology, Engineering, Arts, and Mathematics (STEAM) Education , 2019, Education Sciences.

[21]  Sunghae Jun,et al.  Sustainable Technology Analysis of Artificial Intelligence Using Bayesian and Social Network Models , 2018 .

[22]  Donald W. Loveland,et al.  Automated theorem proving: a logical basis , 1978, Fundamental studies in computer science.

[23]  Judea Pearl,et al.  Fusion, Propagation, and Structuring in Belief Networks , 1986, Artif. Intell..

[24]  Constantin F. Aliferis,et al.  Time and sample efficient discovery of Markov blankets and direct causal relations , 2003, KDD '03.

[25]  Birgit Penzenstadler,et al.  The Rise of Artificial Intelligence under the Lens of Sustainability , 2018, Technologies.

[26]  G. Tzeng,et al.  Advances in Multiple Criteria Decision Making for Sustainability: Modeling and Applications , 2018 .

[27]  P. Gustafson,et al.  The application of Bayesian analysis to issues in developmental research , 2007 .

[28]  Ingemar Johansson Sevä,et al.  The Role of Government in Protecting the Environment: Quality of Government and the Translation of Normative Views about Government Responsibility into Spending Preferences , 2019, International Journal of Sociology.

[29]  J. Pearl Causality: Models, Reasoning and Inference , 2000 .

[30]  Vasile Gherheș,et al.  Technical and Humanities Students’ Perspectives on the Development and Sustainability of Artificial Intelligence (AI) , 2018, Sustainability.

[31]  Marco Casazza,et al.  Enhancing the Sustainability Narrative through a Deeper Understanding of Sustainable Development Indicators , 2017 .

[32]  Yumei Hou,et al.  Measuring Energy Efficiency and Environmental Performance: A Case of South Asia , 2019, Processes.

[33]  John R. Nesselroade,et al.  Bayesian analysis of longitudinal data using growth curve models , 2007 .

[34]  W. D. Hung,et al.  Educational Stakeholders’ Independent Evaluation of an Artificial Intelligence-Enabled Adaptive Learning System Using Bayesian Network Predictive Simulations , 2019, Education Sciences.

[35]  Zuoren Sun,et al.  Regional Differences in Energy and Environmental Performance: An Empirical Study of 283 Cities in China , 2018, Sustainability.

[36]  E. Pajares,et al.  The Tax Burden on Wastewater and the Protection of Water Ecosystems in EU Countries , 2018 .

[37]  George Gadanidis,et al.  Artificial intelligence, computational thinking, and mathematics education , 2017 .

[38]  David J. Spiegelhalter,et al.  Local computations with probabilities on graphical structures and their application to expert systems , 1990 .

[39]  Kevin B. Korb,et al.  Bayesian Artificial Intelligence , 2004, Computer science and data analysis series.

[40]  J. B. Sacomano,et al.  Industry 4.0 and Sustainability Implications: A Scenario-Based Analysis of the Impacts and Challenges , 2018, Sustainability.

[41]  X. Jin Factor graphs and the Sum-Product Algorithm , 2002 .

[42]  Franz J. Neyer,et al.  A Gentle Introduction to Bayesian Analysis: Applications to Developmental Research , 2013, Child development.

[43]  Daniel Dajun Zeng From Computational Thinking to AI Thinking , 2013, IEEE Intell. Syst..

[44]  Louis Rosenberg,et al.  Artificial Swarm Intelligence, a Human-in-the-Loop Approach to A.I , 2016, AAAI.

[45]  L. Ojala,et al.  The World Bank's Logistics Performance Index (LPI) and drivers of logistics performance , 2015 .

[46]  B. Davis Complexity and Education: Vital simultaneities , 2008 .

[47]  Peter Hill,et al.  Preparing for a Renaissance in Assessment , 2014 .

[48]  Concha Bielza,et al.  Comparison of Bayesian networks and artificial neural networks for quality detection in a machining process , 2009, Expert Syst. Appl..

[49]  A. Sperotto,et al.  Water Quality Sustainability Evaluation under Uncertainty: A Multi-Scenario Analysis Based on Bayesian Networks , 2019, Sustainability.

[50]  Xin-Yuan Song,et al.  Evaluation of the Bayesian and Maximum Likelihood Approaches in Analyzing Structural Equation Models with Small Sample Sizes , 2004, Multivariate behavioral research.

[51]  P. Coffey,et al.  The International Bank for Reconstruction and Development: The World Bank , 2006 .

[52]  Judea Pearl,et al.  Causes of Effects and Effects of Causes , 2015 .

[53]  Lei Zou,et al.  Evaluating Land Subsidence Rates and Their Implications for Land Loss in the Lower Mississippi River Basin , 2015 .

[54]  D. Hand,et al.  Idiot's Bayes—Not So Stupid After All? , 2001 .

[55]  Meng-Leong How,et al.  Future-Ready Strategic Oversight of Multiple Artificial Superintelligence-Enabled Adaptive Learning Systems via Human-Centric Explainable AI-Empowered Predictive Optimizations of Educational Outcomes , 2019, Big Data Cogn. Comput..

[56]  I. García‐Sánchez,et al.  Exploring Relationships between Environmental Performance, E-Government and Corruption: A Multivariate Perspective , 2019, Sustainability.

[57]  David J. Spiegelhalter,et al.  Probabilistic Networks and Expert Systems - Exact Computational Methods for Bayesian Networks , 1999, Information Science and Statistics.

[58]  Paul Rad,et al.  AI Thinking for Cloud Education Platform with Personalized Learning , 2018, HICSS.