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

**Authors:** Zhenghua Chen, Min Wu, Alvin Chan, Xiaoli Li, Yew-Soon Ong  
**arXiv:** [2205.03824](https://arxiv.org/abs/2205.03824)  
**Date:** 2022-05-08  
**Summary page:** https://www.summarizepaper.com/en/paper/2205.03824v1/

## Key points

- Artificial Intelligence (AI) is a rapidly growing field with potential benefits for consumers and businesses
- Increased productivity and innovation are among the benefits of AI
- AI has made significant advancements in computer vision, natural language processing, audio analysis, and smart sensing
- Complex and large-scale AI models have enabled solving increasingly complex problems with superior performance and robustness
- The environmental impact of AI development has been substantial, with significant resource consumption
- Concerns about fairness, safety, and privacy in AI have intensified
- The review focuses on addressing the environmental sustainability and social sustainability of AI
- Emerging methodologies and algorithms aim to mitigate the environmental impact of AI while addressing societal concerns
- Existing studies have limitations in addressing sustainability issues
- Potential research challenges and directions for developing sustainable AI techniques are proposed
- The study emphasizes the importance of considering sustainability in AI development
- Insights into current trends and challenges in creating environmentally friendly and socially responsible AI systems are provided

## Summary

Artificial Intelligence (AI) is a rapidly growing field that has attracted significant attention due to its potential to bring numerous benefits to consumers and businesses. These include increased productivity and innovation. AI has made significant advancements in various challenging areas such as computer vision, natural language processing, audio analysis, and smart sensing. These achievements have been made possible by the development of complex and large-scale AI models that can solve increasingly complex problems with superior performance and robustness. However, this rapid progress in AI has come at a cost. The environmental impact of AI development has been substantial, with significant resource consumption. Additionally, concerns about the societal impacts of AI - such as fairness, safety, and privacy - have intensified. These issues pose major challenges for the sustainable development of AI. In this work, Zhenghua Chen, Min Wu, Alvin Chan, Xiaoli Li, and Yew-Soon Ong review the major trends in machine learning approaches that address the sustainability problem of AI. They focus on two key aspects: environmental sustainability and social sustainability of AI. The authors examine emerging methodologies and algorithms that aim to mitigate the environmental impact of AI while also addressing societal concerns. The authors also highlight the limitations of existing studies in addressing these sustainability issues and propose potential research challenges and directions for developing next-generation sustainable AI techniques. By providing this technical review, they aim to promote a sustainable development approach for AI research and development activities within the research community. Overall, this study emphasizes the importance of considering sustainability in AI development. It provides insights into current trends and challenges in creating environmentally friendly and socially responsible AI systems. By addressing these issues proactively <ed>, researchers can ensure that future advancements in AI are not only technologically advanced but also sustainable for our planet <ed> society as a whole.