Philosophers of technology have recently started paying more attention to the environmental impacts of AI, in particular of large language models (LLMs) and generative AI (genAI) applications. Meanwhile, few developers of AI give concrete estimates of the ecological impact of their models and products, and even when they do so, their analysis is often limited to green house gas emissions of certain stages of AI development or use. The current proposal encourages practically viable analyses of the sustainability aspects of genAI informed by philosophical ideas.

Artificial intelligence (AI) has enabled remarkable breakthroughs in numerous areas of critical importance, including tackling global challenges such as climate change. On the other hand, many AI models, especially large generative ones like GPT-4, are trained and deployed on energy-hungry servers in warehouse-scale datacenters, accelerating the datacenter energy consumption at an unprecedented rate.25 As a result, AI's carbon footprint has been undergoing scrutiny, driving the recent progress in AI carbon efficiency.24,31 However, AI's water footprint--many millions of liters of freshwater consumed for cooling the servers and for electricity generation--has largely remained under the radar and keeps escalating. If not properly addressed, AI's water footprint can potentially become a major roadblock to sustainability and create social conflicts, as freshwater resources suitable for human use are extremely limited and unevenly distributed.

Artificial intelligence (AI) is currently considered a sustainability "game-changer" within and outside of academia. In order to discuss sustainable AI this article draws from insights by critical data and algorithm studies, STS, transformative sustainability science, critical computer science, and public interest theory. I argue that while there are indeed many sustainability-related use cases for AI, they are likely to have more overall drawbacks than benefits. To substantiate this claim, I differentiate three 'AI materialities' of the AI supply chain: first the literal materiality (e.g. water, cobalt, lithium, energy consumption etc.), second, the informational materiality (e.g. lots of data and centralised control necessary), and third, the social materiality (e.g. exploitative data work, communities harm by waste and pollution). In all materialities, effects are especially devastating for the global south while benefiting the global north. A second strong claim regarding sustainable AI circles around so called apolitical optimisation (e.g. regarding city traffic), however the optimisation criteria (e.g. cars, bikes, emissions, commute time, health) are purely political and have to be collectively negotiated before applying AI optimisation. Hence, sustainable AI, in principle, cannot break the glass ceiling of transformation and might even distract from necessary societal change. To address that I propose to stop 'unformation gathering' and to apply the 'small is beautiful' principle. This aims to contribute to an informed academic and collective negotiation on how to (not) integrate AI into the sustainability project while avoiding to reproduce the status quo by serving hegemonic interests between useful AI use cases, techno-utopian salvation narratives, technology-centred efficiency paradigms, the exploitative and extractivist character of AI and concepts of digital degrowth.

Advanced Large Language Models (LLMs) have revolutionized various fields, including communication networks, sparking an innovation wave that has led to new applications and services, and significantly enhanced solution schemes. Despite all these impressive developments, most LLMs typically require huge computational resources, resulting in terribly high energy consumption. Thus, this research study proposes an end-to-end pipeline that investigates the trade-off between energy efficiency and model performance for an LLM during fault ticket analysis in communication networks. It further evaluates the pipeline performance using two real-world datasets for the tasks of root cause analysis and response feedback in a communication network. Our results show that an appropriate combination of quantization and pruning techniques is able to reduce energy consumption while significantly improving model performance.

Sustainability encompasses three key facets: economic, environmental, and social. However, the nascent discourse that is emerging on sustainable artificial intelligence (AI) has predominantly focused on the environmental sustainability of AI, often neglecting the economic and social aspects. Achieving truly sustainable AI necessitates addressing the tension between its climate awareness and its social sustainability, which hinges on equitable access to AI development resources. The concept of resource awareness advocates for broader access to the infrastructure required to develop AI, fostering equity in AI innovation. Yet, this push for improving accessibility often overlooks the environmental costs of expanding such resource usage. In this position paper, we argue that reconciling climate and resource awareness is essential to realizing the full potential of sustainable AI. We use the framework of base-superstructure to analyze how the material conditions are influencing the current AI discourse. We also introduce the Climate and Resource Aware Machine Learning (CARAML) framework to address this conflict and propose actionable recommendations spanning individual, community, industry, government, and global levels to achieve sustainable AI.

Mariza Ferro is a professor at the Federal Fluminense University and a visiting professor at Bordeaux University. She has been working in the field of AI since 2002. She works on AI for good, including human-centric AI, ethical and trustworthy AI, green and sustainable AI, and AI for sustainable development goals. She guides her research based on the principle that AI must benefit humankind. Furthermore, she is also working with public outreach by making science available for all.

The increased use of AI systems is associated with multi-faceted societal, environmental, and economic consequences. These include non-transparent decision-making processes, discrimination, increasing inequalities, rising energy consumption and greenhouse gas emissions in AI model development and application, and an increasing concentration of economic power. By considering the multi-dimensionality of sustainability, this paper takes steps towards substantiating the call for an overarching perspective on "sustainable AI". It presents the SCAIS Framework (Sustainability Criteria and Indicators for Artificial Intelligence Systems) which contains a set 19 sustainability criteria for sustainable AI and 67 indicators that is based on the results of a critical review and expert workshops. This interdisciplinary approach contributes a unique holistic perspective to facilitate and structure the discourse on sustainable AI. Further, it provides a concrete framework that lays the foundation for developing standards and tools to support the conscious development and application of AI systems.

The below is a summary of an article about Sustainable AI. As Artificial Intelligence (AI) technology advances and transforms industries, developing and deploying sustainable and environmentally responsible AI is becoming increasingly important. Sustainable AI holds great promise for reducing energy consumption and optimising resource use. However, it can also have unintended consequences that need careful consideration. The carbon footprint of AI is significant, and efforts to address its environmental impact are necessary.

Back in 2019, Gartner predicted that the vast majority of AI projects would continue to fail: Only 53% of projects make it from prototypes to production, and 85% of those blow up. And yet, AI adoption has only accelerated. In an IBM study, 42% organizations reported they're exploring AI, and AI adoption is growing steadily, up four points from 2021. "Very few AI products become successful in creating value for companies, even though companies invest quite a lot of manpower and resources," says Ali Riza Kuyucu, global head of data and analytics at Blue.cloud. "But driving efficiencies through artificial intelligence requires constant monitoring and improvement, or what we call continuous AI -- keeping and sustaining the business value of AI for an organization over a longer period."

Were you unable to attend Transform 2022? Check out all of the summit sessions in our on-demand library now! An organizational movement towards mass digitization is underway -- and no industry is exempt. The number of connected devices is expected to reach 55.7 billion by 2025, of which 75% will be connected to an IoT platform -- a shift that has presented a significant environmental challenge for organizations. The increased demand for data storage and computing power has many questioning their sustainability efforts and raises the question: How can enterprises leverage and implement artificial intelligence (AI) and other smart technology without growing their carbon footprints?