<p dir="ltr">The pursuit of fairness in machine learning (ML) systems is a critical challenge in today’s world that relies heavily on AI systems. However, computing and mitigating the bias necessitates substantial computational resources and time when evaluating across entire datasets. This research introduces an innovative approach to estimate fairness in ML systems by leveraging data characteristics and constructing a metafeatures dataframe. Using our methodology enables the prediction of fairness with significantly reduced computational cost and expedited analysis times. Furthermore, our approach is scalable to different distributions and requires minimal training to deal with out of sample data. This approach not only enhances the efficiency of fairness assessments in ML systems but also provides a scalable framework for future fairness evaluation methodologies. Our findings suggest that using data characteristics to estimate fairness is not only feasible but also effective, offering a promising avenue for developing more equitable ML systems with reduced resource consumption.</p>