Biased Datasets

Biased datasets, containing spurious correlations or skewed representations of certain groups, pose a significant challenge to the fairness and reliability of machine learning models. Current research focuses on developing methods to mitigate these biases, employing techniques like data augmentation, reweighting, and multi-objective optimization within various model architectures including neural networks, generative models, and transformers. These efforts aim to improve model generalization, reduce discriminatory outcomes, and enhance the trustworthiness of AI systems across diverse applications, from hiring processes to medical diagnosis. The ultimate goal is to create more equitable and robust AI systems by addressing the root causes of bias in training data.