In contemporary machine learning, there's a growing trend of utilizing ready-made pre-trained models. In real-world applications, it is essential that the pre-trained models are not just accurate but also demonstrate qualities like fairness. This paper takes a closer look at recently proposed approaches that re-weight the training data to edit a pre-trained model for group fairness. We offer perspectives that unify disparate weighting schemes from past studies and pave the way for new weighting strategies to address group fairness concerns.