The Case for Hybrid Multi-Objective Optimisation in High-Stakes Machine Learning Applications

Abstract

Most classification (supervised learning) algorithms optimise a single objective, typically the predictive performance of the learned classification model. However, in high-stake classification applications, involving e.g. decisions about whether or not an individual should undergo a medical surgery, be granted a loan or be hired for a job, often there is a need to optimise multiple objectives, such as the predictive performance, interpretability or fairness of the learned model. In this context, this position paper discusses the pros and cons of two different multi-objective optimisation approaches (the Pareto and the lexicographic approaches), and proposes a conceptual framework for hybrid multi-objective optimisation, combining those two approaches.