  Artificial Intelligence (AI) is a fast-growing research and development (R&D)
discipline which is attracting increasing attention because of its promises to
bring vast benefits for consumers and businesses, with considerable benefits
promised in productivity growth and innovation. To date it has reported
significant accomplishments in many areas that have been deemed as challenging
for machines, ranging from computer vision, natural language processing, audio
analysis to smart sensing and many others. The technical trend in realizing the
successes has been towards increasing complex and large size AI models so as to
solve more complex problems at superior performance and robustness. This rapid
progress, however, has taken place at the expense of substantial environmental
costs and resources. Besides, debates on the societal impacts of AI, such as
fairness, safety and privacy, have continued to grow in intensity. These issues
have presented major concerns pertaining to the sustainable development of AI.
In this work, we review major trends in machine learning approaches that can
address the sustainability problem of AI. Specifically, we examine emerging AI
methodologies and algorithms for addressing the sustainability issue of AI in
two major aspects, i.e., environmental sustainability and social sustainability
of AI. We will also highlight the major limitations of existing studies and
propose potential research challenges and directions for the development of
next generation of sustainable AI techniques. We believe that this technical
review can help to promote a sustainable development of AI R&D activities for
the research community.
