How Do Companies Manage the Environmental Sustainability of AI? An Interview Study About Green AI Efforts and Regulations

• URL: http://arxiv.org/abs/2505.07317v1
• Date: Mon, 12 May 2025 08:03:55 GMT
• Title: How Do Companies Manage the Environmental Sustainability of AI? An Interview Study About Green AI Efforts and Regulations
• Authors: Ashmita Sampatsing, Sophie Vos, Emma Beauxis-Aussalet, Justus Bogner,
• Abstract summary: We aim to investigate the Green AI perception and management of industry practitioners.<n>We conducted 11 interviews with participants from 10 different organizations that adopted AI-based software.<n>Our findings indicate that 9 of 11 participants prioritized business efficiency during AI adoption, with minimal consideration of environmental sustainability.
• Score: 3.4711214580685557
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With the ever-growing adoption of artificial intelligence (AI), AI-based software and its negative impact on the environment are no longer negligible, and studying and mitigating this impact has become a critical area of research. However, it is currently unclear which role environmental sustainability plays during AI adoption in industry and how AI regulations influence Green AI practices and decision-making in industry. We therefore aim to investigate the Green AI perception and management of industry practitioners. To this end, we conducted a total of 11 interviews with participants from 10 different organizations that adopted AI-based software. The interviews explored three main themes: AI adoption, current efforts in mitigating the negative environmental impact of AI, and the influence of the EU AI Act and the Corporate Sustainability Reporting Directive (CSRD). Our findings indicate that 9 of 11 participants prioritized business efficiency during AI adoption, with minimal consideration of environmental sustainability. Monitoring and mitigation of AI's environmental impact were very limited. Only one participant monitored negative environmental effects. Regarding applied mitigation practices, six participants reported no actions, with the others sporadically mentioning techniques like prompt engineering, relying on smaller models, or not overusing AI. Awareness and compliance with the EU AI Act are low, with only one participant reporting on its influence, while the CSRD drove sustainability reporting efforts primarily in larger companies. All in all, our findings reflect a lack of urgency and priority for sustainable AI among these companies. We suggest that current regulations are not very effective, which has implications for policymakers. Additionally, there is a need to raise industry awareness, but also to provide user-friendly techniques and tools for Green AI practices.

Related papers

• Open and Sustainable AI: challenges, opportunities and the road ahead in the life sciences [50.9036832382286]
  We review the increased erosion of trust in AI research outputs, driven by the issues of poor reusability.<n>We discuss the fragmented components of the AI ecosystem and lack of guiding pathways to best support Open and Sustainable AI.<n>Our work connects researchers with relevant AI resources, facilitating the implementation of sustainable, reusable and transparent AI.
  arXiv  Detail & Related papers  (2025-05-22T12:52:34Z)
• Bridging the Gap: Integrating Ethics and Environmental Sustainability in AI Research and Practice [57.94036023167952]
  We argue that the efforts aiming to study AI's ethical ramifications should be made in tandem with those evaluating its impacts on the environment.<n>We propose best practices to better integrate AI ethics and sustainability in AI research and practice.
  arXiv  Detail & Related papers  (2025-04-01T13:53:11Z)
• From Efficiency Gains to Rebound Effects: The Problem of Jevons' Paradox in AI's Polarized Environmental Debate [69.05573887799203]
  Much of this debate has concentrated on direct impact without addressing the significant indirect effects.<n>This paper examines how the problem of Jevons' Paradox applies to AI, whereby efficiency gains may paradoxically spur increased consumption.<n>We argue that understanding these second-order impacts requires an interdisciplinary approach, combining lifecycle assessments with socio-economic analyses.
  arXiv  Detail & Related papers  (2025-01-27T22:45:06Z)
• Exploring the sustainable scaling of AI dilemma: A projective study of corporations' AI environmental impacts [0.0]
  We propose a methodology to estimate the environmental impact of a company's AI portfolio.<n>Results confirm that large generative AI models consume up to 4600x more energy than traditional models.<n>Mitigating the environmental impact of Generative AI by 2030 requires coordinated efforts across the AI value chain.
  arXiv  Detail & Related papers  (2025-01-24T08:58:49Z)
• Towards an Environmental Ethics of Artificial Intelligence [0.0]
  This paper explores the ethical implications of the environmental impact of Artificial Intelligence (AI) for designing AI systems.<n>We draw on environmental justice literature, in which three categories of justice are distinguished, referring to three elements that can be unjust.<n>Based on these tenets of justice, we outline criteria for developing environmentally just AI systems.
  arXiv  Detail & Related papers  (2024-12-19T17:48:54Z)
• How Performance Pressure Influences AI-Assisted Decision Making [57.53469908423318]
  We show how pressure and explainable AI (XAI) techniques interact with AI advice-taking behavior.<n>Our results show complex interaction effects, with different combinations of pressure and XAI techniques either improving or worsening AI advice taking behavior.
  arXiv  Detail & Related papers  (2024-10-21T22:39:52Z)
• Do Responsible AI Artifacts Advance Stakeholder Goals? Four Key Barriers Perceived by Legal and Civil Stakeholders [59.17981603969404]
  The responsible AI (RAI) community has introduced numerous processes and artifacts to facilitate transparency and support the governance of AI systems. We conduct semi-structured interviews with 19 government, legal, and civil society stakeholders who inform policy and advocacy around responsible AI efforts. We organize these beliefs into four barriers that help explain how RAI artifacts may (inadvertently) reconfigure power relations across civil society, government, and industry.
  arXiv  Detail & Related papers  (2024-08-22T00:14:37Z)
• The Narrow Depth and Breadth of Corporate Responsible AI Research [3.364518262921329]
  We show that the majority of AI firms show limited or no engagement in this critical subfield of AI. Leading AI firms exhibit significantly lower output in responsible AI research compared to their conventional AI research. Our results highlight the urgent need for industry to publicly engage in responsible AI research.
  arXiv  Detail & Related papers  (2024-05-20T17:26:43Z)
• Now, Later, and Lasting: Ten Priorities for AI Research, Policy, and Practice [63.20307830884542]
  Next several decades may well be a turning point for humanity, comparable to the industrial revolution. Launched a decade ago, the project is committed to a perpetual series of studies by multidisciplinary experts. We offer ten recommendations for action that collectively address both the short- and long-term potential impacts of AI technologies.
  arXiv  Detail & Related papers  (2024-04-06T22:18:31Z)
• Balancing Progress and Responsibility: A Synthesis of Sustainability Trade-Offs of AI-Based Systems [8.807173854357597]
  We aim to synthesize trade-offs related to sustainability in the context of integrating AI into software systems. The study was conducted in collaboration with a Dutch financial organization.
  arXiv  Detail & Related papers  (2024-04-05T10:11:08Z)
• Towards Sustainable Artificial Intelligence: An Overview of Environmental Protection Uses and Issues [0.0]
  This paper describes the paradox of an energy-consuming technology serving the ecological challenges of tomorrow. It draws on numerous examples from AI for Green players to present use cases and concrete examples. The environmental dimension is part of the broader ethical problem of AI, and addressing it is crucial for ensuring the sustainability of AI in the long term.
  arXiv  Detail & Related papers  (2022-12-22T14:31:48Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.