A fair classifier chain for multi-label bank marketing strategy classification

Abstract

Recently, the usage of machine learning algorithms is subject to discussion from a legal and ethical point of view. Unwanted discrimination regarding gender or race of a prediction model can lead to legal consequences. Therefore, during predictive model learning, one needs to be aware of possible bias and adjust the model to be fair. However, in bank marketing applications, one customer can receive multiple offers instead of just one. Because of their correlation between, a multi-label classification approach is the most suitable one. This paper proposes a fair classifier chain machine learning model for multi-label classification. Our algorithm solves the multi-label classification problem in an efficient manner, and it is suitable for real-life application employment. The proposed approach allows for controlling fairness constraints during the process of machine learning. It is based on the logistic regression model, thus enabling high efficiency and understandability. We apply our model to a real-life model from bank marketing campaign response prediction. The obtained results are promising. More specifically, our model achieves high fairness measures having an increase from 7% to 17%. However, fairness has a price of a decrease in predictive performance, up to 9% of AUC. To the best of our knowledge, this is the first algorithm that introduce fairness constraints in multi-label classification problems.