The research on fair machine learning (ML) has been growing due to the high demand for unbiased and fair ML models for objective decision-making. Most of this research has been focused on training and tuning the ML model, and less effort has been made to study biases in the processes that clean and prepare data for these models. This paper studies fairness in entity matching (EM), a.k.a. record matching and entity resolution, a primary task in a data cleaning pipeline that can significantly impact ML models’ performance. We introduce a new metric for measuring bias in EM based on Area Under the Curve (AUC) and the risk of record matching between and within subpopulations. We use this metric and real-world data to show biases in a state-of-the-art EM technique. We introduce a debiasing algorithm based on data augmentation (DA) to mitigate bias and conduct experiments to show the algorithm’s effectiveness.