Machine learning engineers' own perception of environmental impacts could be a barrier to the path to greener AI.
A study of 23 machine learning (ML) practitioners around the world conducted by the UK and King's College London highlights alienation from model sustainability and suggests that environmental qualifications were not considered part of AI's performance.
Despite the increasing number of tools to track the environmental impact of AI, responded with comments such as “As an individual, I don't think you can really do much,” suggesting more needs to enable developers to create more greener models.
“It highlights the fundamental lack of agencies at the heart of AI and the central focus of sustainability conversations. Despite tracking information and information about the environmental impact of ML, many developers feel their industry doesn't value sustainability,” said Georgia Panagiotidu, author and lecturer of visualization.
“This work collaborates to identify blockers to achieve spatial change rather than defeating individuals. By integrating sustainable thinking into all AI practices, we can address the lack of knowledge practitioners and provide the tools to make sustainable decisions in the face of the climate crisis.”
I need to do my research and if I tell my supervisor no, I'm not going to use HPC (high-performance computer) because I feel bad for Antarctica penguins.
Anonymous PhD study participants
ML, a subset of AI, has seen major developments in recent years as AI tools play a bigger role in the global economy. This was brought about at a significant environmental cost. Global greenhouse emissions from the ICT industry have doubled over the past decade, with resource needs for ML model training training delaying the retirement of coal power plants.
A scope of ICT companies that train AI To address concerns about three emissions, tools like Code Carbon have been developed to estimate the emissions generated when running code or training AI, giving developers a window into the sustainability of the model.
Research analyzing information given by smart meters influences how consumers make more sustainable decisions, but little work has been done on how eco-feedback tools affect ML practitioners' decisions.
Participants in the paper presented at the 2025 ACM conference on equity, accountability and transparency highlighted technical, personal, regulatory and cultural approaches to reducing carbon emissions, including the use of eco-feedback tools and workplace demands to streamline energy use in AI training.
Nevertheless, most people felt limited responsibility to address environmental concerns, and the responsibility lies with large technical providers of large language models like ChatGpt.
Furthermore, participants described how their field, whether in academia or industry, considered sustainability as “not one of our outcomes, not an indicator of performance,” and was secondary to cultures that increased the accuracy and speed of high models for producing papers and new products.
Responsible AI has taught me that something can actually be deeply embedded around a conversation. Our research highlights that there is something to do to repeat that success story with sustainability. ”
Sinemgörücü
PhD students explained that they were taking the backseat of the competitive environment for research publication research. “I need to do my research and if I'm telling a supervisor no, I'm not going to use HPC (high-performance computer) because I feel bad for Antarctica penguins.
Sinem Görücü, a PhD candidate at King's and the first author of the paper, said, “Qualitative interviews are essential to capturing how each individual thought about their work, and we were amazed at how people found themselves extremely unpowered.
“Responsible AI has taught us that something can actually be embedded deep inside the context of a conversation. Our research emphasizes that there is work to be done to repeat that success story with sustainability.”
In the future, the team hopes to conduct large-scale quantitative research into machine learning practitioners' environmental sustainability awareness.
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