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

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.

Artificial Intelligence Literacy and Sustainable Development: An Ethical Governance and Development Goals Framework

arXiv:2609.10489v1 Announce Type: new Abstract: AI literacy provides foundational competencies that support ethical, transparent, and sustainable technological development, although higher-order capa...

Our contribution to a global environmental standard for AI

Irresponsible AI: big tech's influence on AI research and associated impacts

arXiv:2512. 03077v2 Announce Type: replace-cross Abstract: The accelerated development, deployment and adoption of artificial intelligence systems has been fuelled by the increasing presence of big tech in the AI field.

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.

Environmental Slow AI: Design Principles for Generative Systems

The paper "Environmental Slow AI: Design Principles for Generative Systems" argues that generative AI reflects embedded cultural values that can be reshaped. It proposes five design principles—restraint, sufficiency, selectivity over retention, material visibility, and friction as affordance—grounded in environmental sustainability and the Slow AI tradition. Each principle is illustrated with current system designs and operates at both implementation and interpretive levels to restore human agency and encourage reflective engagement.

Beyond the Last Truffula Tree: SustainAI - A Water-Aware, Closed-Loop Framework for Environmentally Accountable AI

SustainAI is a water‑aware, closed‑loop framework that embeds environmental accountability into AI deployment. It combines real‑time water metering, a hallucination‑aware penalty model, and a water‑aware routing algorithm that considers regional water stress. In tests with small language models, water footprints varied 11‑fold across data centers, and 1,335 inference runs consumed about 399 mL of water but yielded only 240 correct outputs, highlighting the resource cost of inaccurate responses.

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...

AI Scientists as Engines of Discovery: A Case for Development within Reformed Institutions

Agentic artificial intelligence (AI) systems are beginning to assist, accelerate, and partially automate scientific discovery, performing tasks that span literature synthesis, code generation, data analysis, hypothesis proposal, and model criticism. We argue that this transition is qualitative rather than incremental, and that suitably designed multi-agent systems may evolve from passive computational tools into ``AI scientists'' that can expand the hypothesis-generating and verification capacity of science.

Sustainability assessment using multimodal AI agents

arXiv:2507. 17012v2 Announce Type: replace Abstract: Reducing the rapidly growing environmental impact of the computing industry requires assessing the emissions of electronics at scale.

The Industrialization of Research ; On AI-Driven Science and Its Consequences

arXiv:2607. 15164v1 Announce Type: new Abstract: Artificial intelligence is transforming scientific research - not merely as a more powerful instrument, but as an autonomous participant in the research cycle itself.

Carbon-Aware Governance Gates: An Architecture for Sustainable GenAI Development

arXiv:2602. 19718v2 Announce Type: replace-cross Abstract: The rapid adoption of Generative AI (GenAI) in the software development life cycle (SDLC) increases computational demand, which can raise the carbon footprint of development activities.