Bias Mitigation Algorithm

Bias mitigation algorithms aim to remove or reduce unfair biases present in machine learning models, ensuring equitable outcomes across different demographic groups. Current research focuses on developing pre-processing and post-processing techniques, including data augmentation strategies like mixup and novel dropout methods applied during inference, as well as algorithms that minimize discrimination without requiring sensitive attribute information. These advancements are crucial for improving the fairness and trustworthiness of AI systems across various applications, from healthcare and finance to criminal justice, mitigating potential harm caused by biased predictions.