A Comprehensive Sustainable Framework for Machine Learning and Artificial Intelligence

TL;DR

This paper introduces a comprehensive framework called FPIG for sustainable AI, addressing fairness, privacy, interpretability, and emissions simultaneously, and proposes a meta-learning algorithm to optimize model selection based on these pillars.

Contribution

It presents the FPIG framework and a meta-learning algorithm to evaluate and balance key sustainability pillars in AI model development.

Findings

Demonstrates trade-offs between fairness, privacy, interpretability, and emissions.

Shows how the meta-learning algorithm can predict pillar scores for model selection.

Validates the approach on classical and real-world datasets.

Abstract

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.