Section A – Evaluation Setup Section B – Submission Mechanics Section C – Permitted Techniques Section D – What we learned from the 1st Round (Guidelines for final submission) The organisation HuggingFace account to be shared on the submission day. Should you have any further technical questions, please contact the evaluation team: resilientchallenge2026@peren.gouv.fr
The rapid development of artificial intelligence represents a major transformation, affecting economies, public services, and everyday life. Yet AI’s current trajectory faces significant economic, environmental, and accessibility challenges. Developing resilient, sustainable, and efficient AI systems is essential to ensure that AI benefits all people while remaining aligned with environmental and development goals. Model compression is
PURPOSE AND ACCEPTANCE OF THE RULES The purpose of these rules (hereinafter the “Rules”) is to define the regulations and procedures for participation in the Resilient AI Challenge (hereinafter the “Challenge”) organized by the governments of France, India and UNESCO (“the organizers”). This Challenge has been organized with the help of supporters, whose list is
The AI Energy Score initiative, co-led by Hugging Face and Salesforce in partnership with Cohere and Carnegie Mellon University, aims to establish a standardized framework for evaluating the energy efficiency of AI model inference. This project addresses the urgent need to assess and mitigate the environmental impact of AI systems, which are projected to consume significant amounts of energy in the coming years.
The Green Digital Action (GDA) initiative, led by the International Telecommunication Union (ITU) and partners, aims to measure and reduce the environmental impact of AI. Through a three-phase project, it will develop standardized metrics for AI’s energy consumption, water usage, and carbon emissions, fostering sustainable AI practices across sectors.
In the months leading up to the AI Action Summit, a wide range of stakeholders are consulted to elaborate collaboratively a position paper on the current key challenges to foster environmental performance and reduce the environmental impact of AI. The goal is to align internationally on the major challenges to achieve environmental performance of hardware and software and extend the lifespan of equipment and software used for AI.
AI has the potential to tackle critical climate challenges, but its growing energy demands pose significant environmental concerns. The Frugal AI Challenge seeks to promote energy-efficient AI models, proving that impactful solutions can also be sustainable.
The International Energy Agency (IEA), in consultation with industry and key scientific stakeholders, has launched the first global observatory dedicated to energy and Artificial Intelligence, providing a global vision of AI-related energy needs and AI opportunities for the energy sector.
AI is a source of promise to contribute to the objectives of ecological transition. Because public decision-makers and companies must be able to draw on scientific advances to build their strategy around sustainable AI, the international association “Climate Change AI” is listing, on the occasion of the Summit, the 13 key scientific documents to understand the challenge of the environmental sustainability of AI for decision-makers.
A first exploratory workshop on “Access to environmental knowledge through generative AI” addressed the challenges of transforming unstructured environmental documentation into accessible, structured knowledge for feeding Generative AI agents while tackling ethical and technical concerns, such as biases and inconsistencies. Follow-up discussions will help further explore those challenges and opportunities.
The objective of this roadmap is to ensure efficient use of resources, reduce confusion, promote consistency in the measurement of the environmental impact of Artificial Intelligence (AI), and facilitate the widespread adoption of best practices in that regard. Contributors wish to work towards non-conflictual standards to measure the environmental impact of AI and encourage collaboration between international standardization bodies to avoid, as far as possible, the duplication and overlaps of standards.
