Understanding the impossibility of machine learning fairness with data examples

Abstract

Machine learning has become increasingly prevalent in the world as a result of a combination of factors, notably computing breakthroughs and increased data availability. The three most common criteria of fairness in machine learning, despite their common sense and moral appeal, are often mutually exclusive. This frequently presents a challenge and the need to prioritize one criterion over the others. Specifically, the paper highlights the general overview of a machine learning model and presents an example of its application in the legal field that explores the existing biases in models today. We then delve into the three main criteria of machine learning fairness: independence (fairness based on outcomes being unrelated to different characteristics), separation (fairness based on equal error rates across groups), and sufficiency (fairness based on predictions being equally reliable for all groups). In the present experiment, we aimed to investigate the mutual exclusivity of these three machine-learning fairness criteria. We hypothesized that the fairness criteria being evaluated cannot all three be satisfied simultaneously, leading to a machine learning model that remains unfair. The results demonstrated that no threshold in the model simultaneously satisfied independence, separation, and sufficiency, highlighting the limitations of machine learning models that pose various issues across different sectors.