The recent advancements in the field of data-driven algorithms and models have significantly focused on enhancing fairness and accuracy across various applications. A notable trend is the integration of fairness constraints into optimization problems, particularly in graph clustering and recommender systems, to mitigate biases and ensure equitable outcomes. Semidefinite relaxation techniques are being employed to approximate complex optimization problems, offering a balance between clustering quality and fairness. Additionally, the generation of synthetic data through advanced generative models, such as Recurrent GANs and ensemble methods, is proving to be a robust solution for addressing privacy concerns and data scarcity in applications like residential load pattern analysis. These synthetic datasets not only mimic real-world data but also outperform traditional methods in terms of diversity and statistical fidelity. Furthermore, dynamic graph contrastive learning frameworks are being developed to enhance fairness in recommender systems by generating high-quality data augmentations that align with real-world scenarios, thereby improving both fairness and model effectiveness. Overall, the field is moving towards more inclusive and realistic modeling approaches that consider both algorithmic performance and ethical implications.