The field of artificial intelligence is moving towards a greater emphasis on fairness and bias mitigation in decision-making processes. Researchers are exploring the potential of large language models and other machine learning techniques to support fair decision-making, while also identifying and addressing the limitations and biases of these models. A key direction of research is the development of methods to mitigate contextual bias and ensure that AI systems are fair and transparent. This includes the use of feature-wise mixing frameworks, adversarial feature intervention, and audit studies to evaluate and improve the fairness of AI systems. Noteworthy papers in this area include those that introduce innovative methods for fairness testing, such as Meta-Fair, and those that demonstrate the effectiveness of domain-specific modeling and bias auditing in high-stakes domains like hiring. For example, AdFair-CLIP is a novel framework that employs adversarial feature intervention to suppress sensitive attributes and improve prediction fairness in medical diagnostic models. Evaluating the Promise and Pitfalls of LLMs in Hiring Decisions is another notable paper that highlights the importance of domain-specific modeling and bias auditing in hiring scenarios.