SUSTAINABLE AI: STRATEGIES TO REDUCE THE ENVIRONMENTAL FOOTPRINTS OF AI

Abstract

As the demand for artificial intelligence is increasing in modern technology, the emergence of AI in sectors like healthcare, finance, retail and e-commerce, manufacturing, transportation, education, agriculture, energy, law and legal services, entertainment and media, government and public services have been available since recent decades. Despite AI’s extensive impact on these sectors, the environmental impact, primarily through energy consumption and carbon footprints, is the major sustainability issue. The increasing computational requirements for the AI models put a strain on the energy resources, making AI sustainability a vital challenge. The author looks into energy-efficient algorithms, sustainable hardware, and the role of green data centres in supporting AI sustainability. The author also discuss how emerging technologies like quantum computing could improve the energy efficiency of AI systems. By reviewing case studies, such as AI-based optimizations in data centres and machine learning models for energy savings, the author highlights successful examples of AI sustainability. Our findings show that adding sustainable practices to AI development can lower its environmental impact while keeping its performance. This paper highlights the importance of teamwork among researchers, industries, and policymakers to create a more sustainable AI ecosystem for the future.