Artificial Intelligence (AI) is becoming increasingly integral to modern society, yet its environmental impact and ethical implications remain critical concerns. The 1st Workshop on Green-Aware Artificial Intelligence aims to address these challenges by bringing together scholars from various disciplines to explore the intersection of Green AI-which focuses on energy-eficient and environmentally friendly AI systems-and Sustainable AI, which promotes the development of AI technologies that align with human-centered values and broader sustainability goals.
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ceur-ws.org align with ethical and sustainable principles. It covered a broad range of topics, including but not limited to: • Energy-eficient AI Algorithms – Strategies to optimize machine learning models for reduced energy consumption. • Human-centered Green AI Design – Ensuring AI technologies align with human values and ethical considerations. • Ethical Considerations, Sustainability, and Privacy Preservation – Balancing AI advancements with responsible governance. • Reliability, Trustworthiness, and Interpretability in AI Applications – Ensuring robust and trustworthy AI deployments across diferent sectors. • Green Federated Learning and Edge AI – Methods to optimize AI for decentralized and edge-cloud computing environments. • Theoretical Analysis of Energy Eficiency in AI Applications – Exploring the mathematical principles and computational frameworks underlying energy-eficient AI systems. • Green AI Case Studies and Deployments – Real-world applications and lessons learned from sustainable AI implementations. • Sustainable AI Applications in Environmental and Social Sciences, Healthcare, Smart Cities, and
Energy Optimization – Leveraging AI for positive environmental and societal impact. • Parallel and Distributed Algorithms for Energy-eficient AI – Advancing distributed AI computing techniques to enhance energy eficiency and performance. • Energy-aware Training Strategies for Scaling Up Language Models – Investigating the balance between model size, accuracy, and energy eficiency to ensure sustainable scaling. • Energy-aware Strategies to Support AI on Resource-constrained Devices – Developing AI for IoT and low-power devices. • Compression Techniques and Small Language Models – Exploring techniques such as pruning, quantization, and distillation for eficient AI. • Future Trends and Innovations in Green and Sustainable AI – Emerging research directions in AI sustainability.
The 1st Workshop on Green-Aware Artificial Intelligence received a total of 8 paper submissions. Following a rigorous peer-review process, 7 papers were accepted, resulting in an 87.5% acceptance rate, which included 4 regular papers and 3 short papers. These papers reflected a diverse range of contributions within the field of Green and Sustainable AI, showcasing various innovative approaches and solutions for sustainable AI practices. Among the explored topics and research areas, we mention the environmental impact of AI algorithms, energy-eficient optimization techniques, and sustainabilitydriven decision-making frameworks. Several works focused on improving energy consumption in domains such as smart agriculture and sustainable building design, leveraging machine learning models for enhanced eficiency. Others introduced novel approaches for anomaly detection with lightweight feature extraction and meta-learning strategies, enabling more resource-eficient AI systems. Additionally, research examined methods for green-aware temporal reasoning, eficient AI training paradigms, and the identification of key factors that contribute to national sustainability advantages. These contributions highlight the increasing intersection of AI and environmental consciousness, pushing forward innovations that promote sustainable and responsible AI development.
Each submitted paper underwent a single-blind peer review process, where two independent review were conducted for each paper. The workshop chairs made the final acceptance decision, based on the feedback provided by the program committee members. Papers were evaluated based on standard academic criteria, considering originality, technical quality, relevance to the field, potential impact on AI sustainability, and overall clarity. We would like to express our gratitude to the Program Committee members for their dedication in reviewing the submissions and providing insightful feedback. Their expertise and thorough evaluations were crucial in ensuring the quality and academic rigor of the selected papers. We acknowledge the following PC members for their role in the review process:
We would like to express our sincere gratitude to everyone who contributed to the success of the 1st Workshop on Green-Aware Artificial Intelligence .
We thank the Program Committee members and reviewers for their dedication in evaluating submissions and providing constructive feedback. We also extend our appreciation to all authors who submitted their research, as well as to the participants and attendees for their valuable discussions and engagement during the workshop. A special acknowledgment goes to our invited speaker, Prof. Kees van Berkel (TU Wien), for sharing his insightful perspectives on AI Alignment and Normative Reasoning, as well as to the organizing committee of the 23rd International Conference of the Italian Association for Artificial Intelligence . Finally, we acknowledge the support of the PNRR project FAIR - Future AI Research (PE00000013), Spoke 9 - Green-aware AI, under the NRRP MUR program funded by NextGenerationEU.