The growth of machine learning (ML) models and associated datasets triggers a consequent dramatic increase in energy costs for the use and training of these models. In the current context of environmental awareness and global sustainability concerns involving ICT, Green AI is becoming an important research topic. Initiatives like the AI Energy Score Ratings are a good example. Nevertheless, these benchmarking attempts are still to be integrated with existing work on Quality Models and Service-Level Agreements common in other, more mature, ICT subfields. This limits the (automatic) analysis of this model energy descriptions and their use in (semi)automatic model comparison, selection, and certification processes. We aim to leverage the concept of quality models and merge it with existing ML model reporting initiatives and Green/Frugal AI proposals to formalize a Sustainable Quality Model for AI/ML models. As a first step, we propose a new Domain-Specific Language to precisely define the sustainability aspects of an ML model (including the energy costs for its different tasks). This information can then be exported as an extended version of the well-known Model Cards initiative while, at the same time, being formal enough to be input of any other model description automatic process.

翻译：机器学习（ML）模型及相关数据集的增长，导致这些模型使用和训练所需的能源成本急剧增加。在当前涉及信息通信技术（ICT）的环境意识与全球可持续性关切背景下，绿色人工智能正成为一个重要的研究课题。诸如"人工智能能源评分评级"等倡议即是良好范例。然而，这些基准测试尝试仍需与质量模型和服务水平协议等现有工作相整合——这些在其他更成熟的ICT子领域中已普遍应用。这限制了对模型能耗描述的（自动化）分析，以及其在（半）自动化模型比较、选择和认证流程中的应用。我们旨在利用质量模型的概念，将其与现有ML模型报告倡议及绿色/节俭人工智能提案相融合，从而形式化一个面向AI/ML模型的可持续质量模型。作为第一步，我们提出一种新的领域特定语言，用于精确定义ML模型的可持续性维度（包括其不同任务的能源成本）。该信息随后可导出为知名"模型卡"倡议的扩展版本，同时具备足够的形式化程度，可作为任何其他模型描述自动化流程的输入。