| Title: |
Towards a multi-criteria evaluation of the environmental footprint of generative ai services |
| Authors: |
Berthelot, Adrien; Caron, Eddy; Jay, Mathilde; Lefèvre, Laurent |
| Contributors: |
Laboratoire de l'Informatique du Parallélisme (LIP); École normale supérieure de Lyon (ENS de Lyon); Université de Lyon-Université de Lyon-Université Claude Bernard Lyon 1 (UCBL); Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS); OCTO Technology Paris; Algorithms and Software Architectures for Distributed and HPC Platforms (AVALON); Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure de Lyon (ENS de Lyon); Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Centre Inria de Lyon; Institut National de Recherche en Informatique et en Automatique (Inria); Université de Lyon; Université Claude Bernard Lyon 1 (UCBL); Université Grenoble Alpes (UGA); Data Aware Large Scale Computing (DATAMOVE); Centre Inria de l'Université Grenoble Alpes; Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire d'Informatique de Grenoble (LIG); Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP); Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP); ANRT CIFRE N° 2021/0576; BATE project (BATE-UGAREG21A87); ANR-19-P3IA-0003,MIAI,MIAI @ Grenoble Alpes(2019) |
| Source: |
ICT4S 2024 - International Conference on Information and Communications Technology for Sustainability ; https://inria.hal.science/hal-04586653 ; ICT4S 2024 - International Conference on Information and Communications Technology for Sustainability, Jun 2024, Stockholm, Sweden. pp.1-1, 2024 |
| Publisher Information: |
CCSD |
| Publication Year: |
2024 |
| Collection: |
HAL Lyon 1 (University Claude Bernard Lyon 1) |
| Subject Terms: |
Generative AI; Digital services; Life Cycle Analysis; Greenhouse Gas Emission; Energy; Abiotic Depletion Potential; Methodology; [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]; [INFO]Computer Science [cs]; [INFO.INFO-DC]Computer Science [cs]/Distributed; Parallel; and Cluster Computing [cs.DC]; [SDE.ES]Environmental Sciences/Environment and Society |
| Subject Geographic: |
Stockholm; Sweden |
| Time: |
Stockholm, Sweden |
| Description: |
International audience ; As digital services are increasingly being deployed and used in a variety of domains, the environmental impact of Information and Communication Technologies (ICTs) is a matter of concern. Artificial intelligence is driving some of this growth but its environmental cost remains scarcely studied. A recent trend in large-scale generative models such as ChatGPT has especially drawn attention since their training requires intensive use of a massive number of specialized computing resources. Generative AI (Gen-AI) represents a new stage in digital transformation through its many applications. Unfortunately, by accelerating the growth of digital technology, Gen-AI is contributing to the multiple environmental damages caused by its sector. The question of the sustainability of IT must include this new technology and its applications, by estimating its environmental impact. This work proposes a methodology for a multi-criteria evaluation of the environmental impact of generative AI services, considering embodied and usage costs of all the resources required for training models, inferring from them, and hosting them online.Combining life-cycle analysis (LCA) methods and direct measurement experiments, we illustrate our methods by studying Stable Diffusion, an open-source text-to-image Gen-AI model accessible online as a service. This use case is based on an experimental observation of training and inference energy consumption of the model.By calculating the full environmental costs of this Gen-AI service from end to end, we broaden our view of the impact of these technologies. We show that Gen-AI, as a service, generates an impact through the use of numerous user terminals and networks. We also show that decarbonizing the sources of electricity for these services will not be enough to solve the problem of their sustainability, due to their consumption of energy and rare metals. This consumption will inevitably raise the question of feasibility in a world of finite resources. Various scenarios ... |
| Document Type: |
conference object; still image |
| Language: |
English |
| Availability: |
https://inria.hal.science/hal-04586653; https://inria.hal.science/hal-04586653v1/document; https://inria.hal.science/hal-04586653v1/file/POSTER_ICT4S2024%20%281%29.pdf |
| Rights: |
https://creativecommons.org/licenses/by-sa/4.0/ ; info:eu-repo/semantics/OpenAccess |
| Accession Number: |
edsbas.39A8D146 |
| Database: |
BASE |