The SMICT algorithm for enhancing fairness in Dynamic Datasets

Abstract

As machine learning algorithms become more and more prevalent, so do the inherent risks of unfair classification of disadvantaged or underrepresented groups. Additionally, in a dynamic context, the underlying distributions can shift over time, so corrective measures that can work in a static context may end up being detrimental in the long run. In this paper, we propose a new algorithm with the aim to improve fairness in datasets, by modifying the commonly used SMOTE algorithm in a way to work better in a dynamic context, with an added focus on fairness criteria. The results in this paper indicate that this modification, labelled as the SMICT algorithm, can be a promising approach to improving fairness, albeit with limitations and challenges that need to be considered whenever the algorithm is used.