In a world increasingly dominated by AI applications, an understudied aspect
is the carbon and social footprint of these power-hungry algorithms that
require copious computation and a trove of data for training and prediction.
While profitable in the short-term, these practices are unsustainable and
socially extractive from both a data-use and energy-use perspective. This work
proposes an ESG-inspired framework combining socio-technical measures to build
eco-socially responsible AI systems. The framework has four pillars:
compute-efficient machine learning, federated learning, data sovereignty, and a
LEEDesque certificate.
Compute-efficient machine learning is the use of compressed network
architectures that show marginal decreases in accuracy. Federated learning
augments the first pillar's impact through the use of techniques that
distribute computational loads across idle capacity on devices. This is paired
with the third pillar of data sovereignty to ensure the privacy of user data
via techniques like use-based privacy and differential privacy. The final
pillar ties all these factors together and certifies products and services in a
standardized manner on their environmental and social impacts, allowing
consumers to align their purchase with their values.

该研究提出了一个基于 ESG（环境、社会和治理）框架的计算机辅助机器学习算法，用于构建具有可持续发展性质的 AI 系统，并包括四个支柱：计算效率、联邦学习、数据主权和 LEEDesque 认证。