In financial applications, regulations or best practices often lead to specific requirements in machine learning relating to four key pillars: fairness, privacy, interpretability and greenhouse gas emissions. These all sit in the broader context of sustainability in AI, an emerging practical AI topic. However, although these pillars have been individually addressed by past literature, none of these works have considered all the pillars. There are inherent trade-offs between each of the pillars (for example, accuracy vs fairness or accuracy vs privacy), making it even more important to consider them together. This paper outlines a new framework for Sustainable Machine Learning and proposes FPIG, a general AI pipeline that allows for these critical topics to be considered simultaneously to learn the trade-offs between the pillars better. Based on the FPIG framework, we propose a meta-learning algorithm to estimate the four key pillars given a dataset summary, model architecture, and hyperparameters before model training. This algorithm allows users to select the optimal model architecture for a given dataset and a given set of user requirements on the pillars. We illustrate the trade-offs under the FPIG model on three classical datasets and demonstrate the meta-learning approach with an example of real-world datasets and models with different interpretability, showcasing how it can aid model selection.

可持续机器学习是一个新的框架，提出了FPIG通用人工智能流程，可以同时考虑公平性、隐私保护、可解释性以及温室气体排放等重要主题，从而更好地学习这些主题之间的权衡。基于FPIG框架，我们提出了一种元学习算法，用于在模型训练之前，根据数据集摘要、模型架构和超参数估计这四个关键主题。通过在三个经典数据集上展示FPIG模型的权衡，并通过一个实际数据集和具有不同可解释性的模型的例子来展示元学习方法，说明如何辅助模型选择。

机器学习与人工智能综合可持续框架