Dataset Development

Dataset development focuses on creating high-quality, representative data for training and evaluating machine learning models, addressing challenges in data collection, annotation, and ethical considerations. Current research emphasizes structured approaches to dataset design, incorporating scenario-based requirements and rigorous quality control, particularly for applications like autonomous driving and hate speech detection. This work is crucial for improving the reliability, fairness, and generalizability of AI systems across diverse domains, impacting both scientific advancements and real-world applications.