The field of AI fairness is rapidly evolving, with a growing recognition of the need to move beyond traditional quantitative definitions of fairness and towards a more nuanced, context-aware approach. This shift is driven by the realization that bias and fairness are complex, multifaceted issues that cannot be reduced to simple mathematical formulations. Instead, researchers are increasingly drawing on philosophical theories, empirical evidence, and social science perspectives to develop more sophisticated frameworks for understanding and addressing bias in AI systems. A key area of focus is the development of bias mitigation strategies that can be applied in a variety of domains, from housing price prediction to recidivism risk assessment. Notably, some papers are making significant contributions to this field, including one that proposes a framework for embracing corrective, intentional biases to promote genuine equality of opportunity. Another noteworthy paper investigates the performance of different bias mitigation solutions in ML-driven house price prediction models, finding that in-processing approaches tend to be more effective than pre-processing ones. Overall, the field is moving towards a more comprehensive and contextual understanding of AI fairness, one that recognizes the importance of systemic thinking and the need to address the root causes of bias and inequality.