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Artificial Intelligence (AI) is a fast-growing research and development (R&D)discipline which is attracting increasing attention because of its promises tobring vast benefits for consumers and businesses, with considerable benefitspromised in productivity growth and innovation. To date it has reportedsignificant accomplishments in many areas that have been deemed as challengingfor machines, ranging from computer vision, natural language processing, audioanalysis to smart sensing and many others. The technical trend in realizing thesuccesses has been towards increasing complex and large size AI models so as tosolve more complex problems at superior performance and robustness. This rapidprogress, however, has taken place at the expense of substantial environmentalcosts and resources. Besides, debates on the societal impacts of AI, such asfairness, safety and privacy, have continued to grow in intensity. These issueshave presented major concerns pertaining to the sustainable development of AI.In this work, we review major trends in machine learning approaches that canaddress the sustainability problem of AI. Specifically, we examine emerging AImethodologies and algorithms for addressing the sustainability issue of AI intwo major aspects, i.e., environmental sustainability and social sustainabilityof AI. We will also highlight the major limitations of existing studies andpropose potential research challenges and directions for the development ofnext generation of sustainable AI techniques. We believe that this technicalreview can help to promote a sustainable development of AI R&D activities forthe research community.

A Survey on AI Sustainability: Emerging Trends on Learning Algorithms and Research Challenges