Preface to the Proceedings of Green-Aware AI 2024 Riccardo Cantini1 , Davide Mario Longo1,∗ and Dipanwita Thakur1 1 University of Calabria, Rende, Italy 1. Introduction 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-efficient 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. The importance of this workshop is underscored by recent advancements and challenges in AI sustainability. As AI models grow larger and more resource-intensive, concerns about their energy consumption and carbon emissions have intensified. At the same time, the need for low-power AI solutions for edge computing and IoT devices has created new opportunities for research in energy- efficient algorithms and hardware. Furthermore, ethics and responsibility in AI sustainability are gaining traction, emphasizing the importance of aligning AI development with principles such as fairness, transparency, and accountability. The primary objective of this workshop is to build a research community focused on advancing Green and Sustainable AI and to foster interdisciplinary discussions, enabling participants to exchange ideas, share research findings, and explore collaborative opportunities. The workshop was co-located with the 23rd International Conference of the Italian Association for Artificial Intelligence (AIxIA 2024), held from November 25–28, 2024, in Bolzano, Italy. This event provided a broader academic context, enabling cross-disciplinary engagement between AI sustainability researchers, ethicists, and industry practitioners. A key highlight of the event was an invited talk by Prof. Kees van Berkel (TU Wien), titled AI Alignment and Normative Reasoning. His presentation explored the ethical dimensions of AI sustainability, emphasizing how AI systems must be aligned with human values, ethics, and laws. In particular, he highlighted the role of normative reasoning and conflict resolution mechanisms in ensuring that AI operates within ethical and sustainable boundaries. This workshop represents a crucial step in fostering a community committed to sustainable and ethical AI development. By tackling the technical and ethical challenges of energy-efficient AI and ensuring that technologies align with human values, it lays the foundation for responsible innovation. It may also inspire future research and collaboration in advancing Green and Sustainable AI, fostering the development of innovative solutions that balance technological progress with ethical, social, and environmental responsibility. 2. Workshop Scope and Themes The 1st Workshop on Green-Aware Artificial Intelligence focuses on the intersection of AI, sustainability, and energy efficiency, bringing together researchers from diverse fields to explore new methods and technologies that promote environmentally responsible AI development. The workshop addresses key challenges in reducing AI’s carbon footprint, improving energy efficiency, and ensuring AI systems 1st Workshop on Green-Aware Artificial Intelligence, 23rd International Conference of the Italian Association for Artificial Intelligence (AIxIA 2024), November 25–28, 2024, Bolzano, Italy ∗ Corresponding author. Envelope-Open rcantini@dimes.unical.it (R. Cantini); davidemario.longo@dimes.unical.it (D. M. Longo); dipanwita.thakur@dimes.unical.it (D. Thakur) Orcid 0000-0003-3053-6132 (R. Cantini); 0000-0003-4018-4994 (D. M. Longo); 0000-0003-2895-1425 (D. Thakur) © 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0). CEUR ceur-ws.org Workshop ISSN 1613-0073 Proceedings align with ethical and sustainable principles. It covered a broad range of topics, including but not limited to: • Energy-efficient 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 trustwor- thy AI deployments across different sectors. • Green Federated Learning and Edge AI – Methods to optimize AI for decentralized and edge-cloud computing environments. • Theoretical Analysis of Energy Efficiency in AI Applications – Exploring the mathematical principles and computational frameworks underlying energy-efficient 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-efficient AI – Advancing distributed AI computing techniques to enhance energy efficiency and performance. • Energy-aware Training Strategies for Scaling Up Language Models – Investigating the balance between model size, accuracy, and energy efficiency 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 efficient AI. • Future Trends and Innovations in Green and Sustainable AI – Emerging research directions in AI sustainability. 3. Contributions and Paper Selection The 1st Workshop on Green-Aware Artificial Intelligence received a total of 8 paper submissions. Fol- lowing 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-efficient optimization techniques, and sustainability- driven 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 efficiency. Others introduced novel approaches for anomaly detection with lightweight feature extraction and meta-learning strategies, enabling more resource-efficient AI systems. Addition- ally, research examined methods for green-aware temporal reasoning, efficient AI training paradigms, and the identification of key factors that contribute to national sustainability advantages. These contri- butions 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. 3.1. Program Committee 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: • Ram Sarkar, Jadavpur University, West Bengal, India • Debashis De, Maulana Abul Kalam Azad University of Technology, West Bengal, India • Saroj Biswas, NIT Silchar, India • Suparna Biswas, Maulana Abul Kalam Azad University of Technology, West Bengal, India • Sandip Roy, Old Dominion University, USA • Alessio Orsino, University of Calabria, Italy • Paolo Lindia, University of Calabria, Italy • Cristian Cosentino, University of Calabria, Italy • Francesco De Luca, University of Calabria, Italy • Simone Amirato, University of Calabria, Italy • Eda Cicek, TU Wien, Austria • Stefan Woltran, TU Wien, Austria • Francescomaria Faticanti, École Normale Supérieure de Lyon, France • Dakshine Ranjan Kisku, NIT Durgapur, India • Sarada Prasad Gochhayat, IIT Jammu, India • Bikash Chanda Singh, Old Dominion University, USA • Simona Nisticò, University of Calabria, Italy • Luca Ferragina, University of Calabria, Italy 4. Acknowledgments 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 sub- missions 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.