The recent advancements in algorithmic fairness have seen a shift towards more nuanced and robust methods for addressing bias in machine learning models. Researchers are increasingly focusing on conditional fairness metrics, which extend beyond traditional demographic parity to consider the impact of additional features on model outcomes. This approach is particularly valuable in scenarios where the conditioning variables are complex and continuous, as it allows for a more granular assessment of fairness. Additionally, there is a growing interest in directly manipulating model parameters to mitigate bias, rather than relying on indirect methods such as sample reweighting or predefined bias constraints. This parameter-space approach offers greater control over the learning process and can lead to models that are both more accurate and fair. Furthermore, the integration of bilevel optimization techniques in fairness-aware machine learning is emerging as a promising direction, enabling a better balance between accuracy and fairness. These developments collectively aim to create more equitable and effective machine learning systems, particularly in sensitive applications such as classification tasks involving diverse groups. Notably, the introduction of methods that can simultaneously enhance fairness and privacy in large language models represents a significant step forward in addressing the ethical implications of AI systems.