Sample Specific Debiasing

Sample-specific debiasing aims to correct for biases in datasets used to train machine learning models, improving the fairness, accuracy, and generalizability of these models. Current research focuses on developing methods to address various biases, including those stemming from data imbalances, spatial artifacts in images, and the inherent limitations of sampling techniques, employing techniques like data augmentation with generative models (e.g., using ChatGPT), neural network architectures designed for robust feature extraction, and sample-weighting strategies. These advancements are crucial for mitigating the harmful effects of biased data in diverse applications, ranging from deepfake detection and large language model fairness to accurate population statistics estimation and reliable medical image analysis.