The fairness-accuracy tradeoff refers to the tension between achieving equitable outcomes across different groups and maintaining high predictive performance in machine learning models. Balancing these objectives often requires sacrificing some degree of accuracy to ensure that the model does not disproportionately disadvantage any particular group.