Green and Responsible AI: Emerging Trends, Challenges, and Future Directions in Sustainable Computing
TL;DR
This review examines Green AI as an emerging paradigm for developing energy-efficient machine learning systems and explores its intersection with the principles of responsible AI, providing a roadmap for researchers and practitioners seeking to develop AI systems that balance computational performance with environmental sustainability and societal responsibility.
Abstract
The exponential growth of artificial intelligence (AI) has created unprecedented computational demands, raising important concerns about environmental sustainability and responsible deployment. This review examines Green AI as an emerging paradigm for developing energy-efficient machine learning systems and explores its intersection with the principles of responsible AI. The review analyzes the computational costs of large-scale model training, approaches for quantifying carbon footprints, and strategies for reducing environmental impacts throughout the AI lifecycle. Key challenges include hardware optimization, algorithmic efficiency, data center management, and socio-technical concerns related to fairness, transparency, accountability, and ethical governance. Current trends in sustainable computing architectures, federated learning for edge deployment, model compression, and renewable energy integration are also examined. Future research directions include policy development, standardization, and stronger interdisciplinary collaboration among computer scientists, environmental researchers, and policymakers. Overall, this review provides a roadmap for researchers and practitioners seeking to develop AI systems that balance computational performance with environmental sustainability and societal responsibility.
