Title: Generative Fairness Teaching. Abstract: Increasing evidences has shown that data biases towards sensitive features such as gender or race are often inherited or even amplified by machine learning models. Recent advancements in fairness mitigate such biases by adjusting the predictions across sensitive groups during the training. Such a correction, however, can only take advantage of samples in a fixed dataset, which usually has limited amount of samples for the minority groups. We propose a generative fairness teaching framework that provides a model with not only real samples but also synthesized samples to compensate the data biases during training. We employ such a teaching strategy by implementing a Generative Fairness Teacher (GFT) that dynamically adjust the proportion of training data for a biased student model. Experimental results indicated that our teacher model is capable of guiding a wide range of biased models by improving  the fairness and performance trade-offs significantly.