Representative Dataset

Representative datasets are crucial for training robust and unbiased machine learning models, ensuring that models generalize well to real-world scenarios and avoid skewed performance across different subgroups. Current research focuses on developing methods for creating such datasets, including strategies for data collection, annotation, and augmentation tailored to specific domains (e.g., agriculture, healthcare, and various language groups), as well as algorithms to assess dataset representativeness and guide data acquisition. The availability of high-quality, representative datasets is essential for advancing machine learning across diverse scientific fields and practical applications, improving the reliability and fairness of AI systems.