The recent advancements in fairness in machine learning have seen a significant shift towards addressing biases without compromising model utility. Researchers are increasingly focusing on novel frameworks that balance fairness and utility through innovative distillation techniques and statistical measures. These approaches often leverage dual-teacher models and causal graph models to guide the learning process, ensuring that fairness is embedded without sacrificing the model's predictive power. Additionally, there is a growing interest in extending fairness notions to account for downstream effects, such as strategic adaptations by individuals in response to model predictions, thereby ensuring a more holistic fairness perspective. Tools and packages are also being developed to facilitate fair classification, with a focus on modular stages that address unfairness at different phases of the learning process. Furthermore, the importance of data acquisition in improving model fairness is being recognized, with reinforcement learning techniques being employed to select the most beneficial data points for training. Overall, the field is moving towards more nuanced and comprehensive fairness solutions that consider the broader implications of model predictions and the dynamic nature of data acquisition.