Dataset Bias

Dataset bias, the presence of spurious correlations or skewed representations within training data, significantly hinders the generalization and fairness of machine learning models. Current research focuses on identifying and mitigating these biases through various techniques, including data augmentation (e.g., using diffusion models or counterfactual examples), algorithmic adjustments (e.g., re-weighting samples, employing adversarial training), and the development of new evaluation metrics to better assess real-world bias. Addressing dataset bias is crucial for building reliable and equitable AI systems across diverse applications, particularly in sensitive domains like healthcare and law enforcement, where biased models can have significant societal consequences.