Error Prone Group

Error-prone groups, subsets of data exhibiting disproportionately high error rates in machine learning models, are a central concern in ensuring fairness and robustness. Research focuses on identifying these groups, often through adversarial methods or analysis of group-specific error differences, and mitigating their impact using techniques like weighted regularization or group-distributionally robust optimization. Understanding and addressing these errors is crucial for improving model reliability across diverse populations and preventing algorithmic bias in applications ranging from biometric verification to healthcare.