  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.
