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.