Artificial Intelligence and Machine Learning Innovation in SDGs
Ambar Yoganingrum, Rulina Rachmawati, Cahyo Trianggoro, Arafat Febriandirza, Koharudin Koharudin, Muhammad Yudhi Rezaldi, Abdurrakhman Prasetyadi
Copyright: © 2023
|Pages: 18
DOI: 10.4018/978-1-7998-9220-5.ch154
Abstract
Artificial intelligence and machine learning have become prominent fields of science and are believed to be powerful tools to achieve the Sustainable Development Goals (SDGs). Therefore, it is necessary to discuss the relationship of AI and ML to the SDGs. This chapter aims to provide information about the focus of AI and ML research on the 17 SDGs. This article finds that the amount of AI and ML research for several SDGs is very high.
TopBackground
AI and ML have become very popular recently and are increasingly becoming a part of our daily life. Figure 1 shows the increasing trend in the number of publications discussing AI and ML across the 17 SDGs. Data were taken from Scopus. The graph illustrates that some goals have long been of interest to AI developers, while others have only been in the last few years.
There have been many studies discussing the role of AI in the achievement of the SDGs. Among them is the role of AI in achieving 137 targets and failing to achieve 59 targets of SDGs (Vinuesa et al., 2020). Then, the role of AI in SDGs as an enabler to address research gaps in academic research, funding institutions, professionals, and industry, with an emphasis on the transportation sector (Gupta et al., 2021). Meanwhile, Sætra (2021) uses the concept of SDGs to provide a new and useful framework for analyzing and categorizing the benefits and harms of AI. To the best of the author's knowledge, the focus of AI research on each of the SDGs has not been identified. This topic is important for analyzing the areas of the SDGs that AI developers are most interested in. In addition to providing information on some issues that AI has not approached. Using a bibliometrics approach, this chapter will provide a brief overview of the areas supported by AI and ML for each SDG. Furthermore, the paper applied narrative review to show the core research of AI and ML in the 17 SDGs and discuss the required future research direction. This study contributes to the academic community, regarding the focus of AI and ML research on the SDGs.
Figure 1. Illustration of the growing number of papers discussing AI and ML for the SDGs
TopAi And Ml Innovation In The 17 Sdgs
Poverty
No poverty is the first goal of the SDGs. The purpose of this goal is to eradicate poverty in all forms around the world by 2030. Practitioners and policymakers address this goal by using AI and ML techniques to assist in decision-making, estimate the poverty level of vulnerable areas, and monitor progress toward poverty alleviation programs. Several AI and ML that have been developed include a tool that combines spatial interpolation approaches and applied machine learning modeling methods based on the Bayesian geostatistical (BGS) and Artificial neural networks (ANN) to estimate the development over small areas not captured on a national scale, based on socio-economic indicators. Then a tool to evaluate urbanism's effects on the economic dynamism of a neighborhood. The tool could help the government develop poverty prevention programs in mixed neighborhoods. Next, Automated Machine Learning (AutoML) measures poverty risks by evaluating over-indebted household profiles. The technique allowed us to distinguish clusters of over-indebtedness (e.g., low-income, low credit control, and household crisis) and predict families’ over-indebtedness risk factors. Hence, it contributes to identifying early-stage poverty.
Key Terms in this Chapter
Machine Learning: Is a subfield of artificial intelligence where machines are trained to imitate human intelligence, thereby being able to perform complex tasks.
Sustainable Development Goals: Also known as universal calls for all levels of society to act, end poverty, and protect the earth. Therefore by 2030, everyone will enjoy peace and prosperity.
Artificial Intelligence: A computer or robot that requires human intelligence and ingenuity to perform tasks normally performed by humans.
Internet of Things: Describes a network of physical objects, where those objects are connected to sensors, software, and other technologies to exchange data over the internet.
Disinfection By-Products (DBP): Generally formed by the reaction of disinfectants such as chlorine with organic precursors present in water, where these precursors act as precursors of DBP.