Biased Data

Biased data, stemming from skewed sampling or inherent societal prejudices, significantly impacts the fairness and accuracy of machine learning models across diverse applications, from healthcare recommendations to hiring processes. Current research focuses on identifying and mitigating these biases through techniques like data augmentation, debiasing algorithms (including variational autoencoders and self-supervised adversarial training), and fairness-aware model training, often employing transformer-based models and convolutional neural networks. Addressing data bias is crucial for ensuring equitable outcomes in AI systems and improving the reliability of scientific findings derived from data-driven analyses.