Survey on AI sustainability: emerging trends on learning algorithms and research challenges

• Main Authors: Chen, Zhenghua, Wu, Min, Chan, Alvin, Li, Xiaoli, Ong, Yew-Soon
• Other Authors: School of Electrical and Electronic Engineering
• Format: Article
• Language: English
• Published: 2023
• Subjects: Engineering::Computer science and engineering; Artificial Intelligence; Sustainable Development
• Online Access: https://hdl.handle.net/10356/170678
• Institution: Nanyang Technological University

Artificial Intelligence (AI) is a fast-growing research and development (RandD) discipline which is attracting increasing attention because it promises to bring vast benefits for consumers and businesses, with considerable benefits promised in productivity growth and innovation. To date, significant accomplishments have been reported in many areas that have been deemed challenging for machines, ranging from computer vision, natural language processing, audio analysis to smart sensing and many others. The technology trend in realizing success has developed towards increasingly complex and large-size AI models 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. In addition, debates on the societal impacts of AI, such as fairness, safety, and privacy, have continued to grow in intensity. These issues have reflected major concerns pertaining to the sustainable development of AI. In this work, major trends in machine learning approaches that can address the sustainability problem of AI have been reviewed. Specifically, the emerging AI methodologies and algorithms are examined for addressing the sustainability issue of AI in two major aspects, i.e., environmental sustainability and social sustainability of AI. Then, the major limitations of the existing studies are highlighted, and potential research challenges and directions are proposed for the development of the next generation of sustainable AI techniques. It is believed that this technical review can help promote a sustainable development of AI RandD activities for the research community.