Our contribution to a global environmental standard for AI

Position: Neglecting the Sustainability of AI is Fuelling a Global AI Arms Race

arXiv:2502. 20016v2 Announce Type: replace Abstract: Sustainability encompasses three key facets: economic, environmental, and social.

AI in abundance

The Future of the Global Open-Source AI Ecosystem: From DeepSeek to AI+

A Checklist to assess the energy and carbon impacts of ML/AI applications in Earth System Modeling

arXiv:2609.00847v1 Announce Type: cross Abstract: As machine learning and artificial intelligence find their way into nearly every aspect of climate, weather, and Earth system modeling, it is worth p...

Sustainability and Artificial Intelligence: Necessary, Challenging, and Promising Intersections

arXiv:2606. 09006v1 Announce Type: cross Abstract: Both digital economy and digital technology researchers increasingly recognize the need to better address the role that artificial intelligence (AI) plays in shaping the evolution of the environmental, social and governance aspects of development.

Building standards for the next phase of AI

OpenAI outlines a path to shared global AI standards, calling for coordinated evaluation, reporting, and governance to improve safety.

Strengthening America’s AI leadership with the U.S. National Laboratories

OpenAI’s latest line of reasoning models will be used by nation’s leading scientists to drive scientific breakthroughs.

Towards Sustainable Artificial Intelligence: A Comprehensive Review and Comparative Analysis of Deep Learning Models' Carbon Footprint

arXiv:2608. 09998v1 Announce Type: new Abstract: Artificial Intelligence (AI) and Machine Learning (ML) have become powerful tools for supporting and automating complex human tasks.

Position: AI Must Become Planet-Centered, Not Just Human-Centered

arXiv:2606. 13704v1 Announce Type: cross Abstract: This position paper argues that contemporary AI paradigms are insufficient for supporting complex global goals and introduces Planet-Centered AI (PCAI) as a design philosophy and research agenda that reorients AI toward planetary-scale socio-ecological systems and their long-term trajectories.

Beyond State-of-the-Art: Standardising Environmental Impact Metrics for AI Research

As the capabilities and ubiquity of Large Language Models (LLMs) grow, so does their environmental footprint. Despite calls for responsible AI, the machine learning community lacks standardised practi...

Beyond State-of-the-Art: Standardising Environmental Impact Metrics for AI Research

The paper highlights that as Large Language Models grow in capability and prevalence, their environmental footprint is increasing, yet the machine learning community lacks standardized carbon accounting practices. An automated review of 5,285 NeurIPS 2025 papers shows almost no reporting of environmental impact. To address this, the authors propose standardized sustainability metrics for training efficiency, heuristics for estimating inference carbon costs, a software tool called carbonbenchmark for tracking emissions, and the SMAJ framework to encourage prioritizing computational efficiency and environmental accountability over marginal accuracy gains.

Import AI 475: Swarm scaling; Google DeepMind watermarks biology; and the AI science economy

Import AI 475 covers several current AI topics, including swarm scaling, Google DeepMind’s use of watermarks in biology, and the broader AI science economy. The episode raises questions about decision‑making in AI deployment. It highlights the importance of understanding who determines the roles and limits of AI systems.