Guest Editors
Prof. Ka-Chun Wong, City University of Hong Kong, China
Prof. Man-Ching Yuen, Hong Kong Shue Yan University, China
Dr. Xingjian Chen, Harvard University, USA
Summary
Background: According to United Nations, they have proposed Sustainable Development Goals (SDGs) as a blueprint for addressing the world's most pressing challenges. The recent advent of Machine Learning (ML) technologies has the potential to significantly contribute to these goals. In particular, ML systems can learn from data and make predictions or decisions without being explicitly programmed.
Current Research Progress: There has been substantial progress in applying ML to various aspects of the SDGs. For example, ML algorithms have been leveraged for predicting poverty levels using satellite imagery, optimizing renewable energy production, and developing predictive models for disease outbreaks. Furthermore, natural language processing, a branch of ML, has been used to analyze public sentiment towards environmental policies. However, the application of ML in the context of SDGs is still in its nascent stages, and its potential is far from fully realized.
Directions for Improvement: Future research needs to focus on developing more sophisticated ML models capable of handling the complexity and scale of SDG-related challenges. There is also a need for interdisciplinary research that combines expertise in ML with domain-specific knowledge in areas relevant to the SDGs, such as environmental science, public health, and social policy. Additionally, addressing issues related to data quality, privacy, and ethical considerations is crucial for the responsible use of ML in this context.
Scope of the Special Issue: This special issue invites original research and review articles that explore the use of ML in advancing the SDGs. Topics of interest include, but are not limited to: ML applications in climate change modeling and mitigation; use of ML in predicting and addressing health and socioeconomic inequalities; ML-based solutions for sustainable cities and communities; and ethical, legal, and social implications of ML applications in the context of the SDGs. We encourage submissions that demonstrate innovative uses of ML techniques, address methodological challenges in this field, and offer critical perspectives. According to the definitions of SDGs, the topics of interest include but are not limited to:
Machine Learning in Social Justice and Equality
Machine Learning in Good Health and Well-being
Machine Learning in Quality Education
Machine Learning in Clean Water and Sanitation
Machine Learning in Affordable and Clean Energy
Machine Learning in Industrial Innovation and Infrastructure
Machine Learning in Sustainable Cities and Communities
Machine Learning in Environmental Protection
Machine Learning in Life on Land
Machine Learning in Peace and Strong Institutions
Machine Learning in Partnerships and Collaborations
Keywords
Machine Learning
Data Science
Artificial Intelligence
Sustainable Development Goals
Climate Change
Medicinal Informatics
Health Informatics
AI Ethics and Regulation
Socioeconomic Inequalities
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