Towards Sustainability Model Cards

2025-07-25

AI Summary

Current Green AI research lacks integration with established quality models and service-level agreements (SLAs) from the ICT domain, hindering automated analysis, comparison, and certification of machine learning model energy consumption. Method: We propose a sustainability quality model that unifies Green AI principles with standardized model reporting frameworks (e.g., Model Cards) and design a domain-specific language (DSL) for formal modeling of energy-related characteristics. This enables structured specification of energy metrics, cross-model automated benchmarking, and verifiable sustainability compliance assessment. Contribution/Results: First, we systematically introduce quality model theory into Green AI evaluation. Second, we extend Model Cards into an extensible, executable format supporting rigorous energy-aware documentation. Third, we establish a practical technical foundation for energy efficiency transparency, model selection optimization, and green certification of AI systems—bridging the gap between sustainability goals and operational ICT governance.

Technology Category

• Machine Learning: Efficient ML / Green AI
• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness
• Knowledge Representation and Reasoning: Qualitative Reasoning

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

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.

Problem

• Addressing energy costs in ML model training and usage
• Integrating sustainability metrics with ML model reporting
• Developing a formal language for ML sustainability aspects

Innovation

• Develops Sustainable Quality Model for AI
• Introduces Domain-Specific Language for ML
• Extends Model Cards with energy metrics