The Role of Machine Learning Algorithms in Driving Sustainable Development Goals: A Comprehensive Survey

Abstract

The United Nations (UN) sustainable development goals (SDGs) are our common goal to end poverty, protect the planet, and ensure peace and prosperity by 2030. Technology adoption by the masses, more so Machine learning (ML), can enable these SDGs. This survey examines the various ways in which ML algorithms are working towards SDGs for various sectors such as health, education, energy and the environment. ML can deal with complex data sets. It can predict behavior and optimize decision-making. Thus, it can play an important role in solving global issues. In health care ML helps to improve the disease diagnosis, epidemic prediction and personalized medicine for better health care. Adaptive learning software and predictive analytics powered by ML will enhance the learning experience while minimizing dropouts. In the energy sector, ML can help manage the grid smartly, predict renewable energies, and optimize resources. Thus, it can make energy cheaper and cleaner.  ML applications in the environment include climate modeling and deforestation monitoring to aid climate action.  ML has a lot of potential but is still being held back by data scarcity and algorithmic bias. Moreover, developing countries face resource constraint issues. It is important to ensure responsible implementation by setting effective measures to address ethical concerns. This study shows that we need interdisciplinary collaboration, scalable solutions, policy integration, etc., if ML is to have maximal impact on the SDGs. ML can help create a sustainable and equitable future by overcoming these challenges and boosting innovations in key sectors. ML has the potential of helping us achieve great things. However, the same ML can cause a lot of damage if not used responsibly.