Towards Sustainability Model Cards

TL;DR

This paper proposes a formalized approach to sustainability in AI/ML models by introducing a new language and extending Model Cards to better capture energy and environmental aspects for comparison and certification.

Contribution

It introduces a domain-specific language for defining sustainability aspects of ML models and extends Model Cards to incorporate environmental metrics systematically.

Findings

A new domain-specific language for sustainability in ML models.

Extension of Model Cards to include energy and environmental metrics.

Facilitates automatic analysis and comparison of ML models based on sustainability.

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.