一句话：提出 Green AI Quotient 指标与方法，量化 AI 系统及其开发过程的绿色程度，并通过实证验证其促进可持续实践。
随着 AI 在温室气体与碳排放中的角色受到关注，能效正成为 AI 研发的核心指标，可持续性也被视为软件工程的重要非功能需求。然而，如何量化 AI 系统及其生命周期中对“绿色”实践的采纳，仍缺乏指标与方法，导致研究与工业落地之间存在鸿沟。本文提出一种评估 AI 系统及其开发过程“绿色程度”的方法论与新指标——Green AI Quotient。该指标基于节能 AI 研究与最佳实践构建，旨在为 AI 从业者的绿色 AI 之旅提供可度量的关键步骤。实证验证表明，Green AI Quotient 能够鼓励可持续实践的采用，并提升 AI 生命周期中相关实践的认知。
As the world takes cognizance of AI's growing role in greenhouse gas(GHG) and carbon emissions, the focus of AI research & development is shifting towards inclusion of energy efficiency as another core metric. Sustainability, a core agenda for most organizations, is also being viewed as a core non-functional requirement in software engineering. A similar effort is being undertaken to extend sustainability principles to AI-based systems with focus on energy efficient training and inference techniques. But an important question arises, does there even exist any metrics or methods which can quantify adoption of “green” practices in the life cycle of AI-based systems? There is a huge gap which exists between the growing research corpus related to sustainable practices in AI research and its adoption at an industry scale. The goal of this work is to introduce a methodology and novel metric for assessing “greenness” of any AI-based system and its development process, based on energy efficient AI research and practices. The novel metric, termed as Green AI Quotient, would be a key step towards AI practitioner's Green AI journey. Empirical validation of our approach suggest that Green AI Quotient is able to encourage adoption and raise awareness regarding sustainable practices in AI lifecycle.
@inproceedings{SikandSKP23,
title = {Green AI Quotient: Assessing Greenness of AI-based software and the way forward},
author = {Samarth Sikand and Vibhu Saujanya Sharma and Vikrant Kaulgud and Sanjay Podder},
booktitle = {ASE},
year = {2023},
doi = {10.1109/ase56229.2023.00115}
}