Label Reliability

Label reliability, the accuracy and consistency of annotations used to train machine learning models, is crucial for building effective and trustworthy AI systems. Current research focuses on improving label quality through techniques like incorporating diverse perspectives to resolve disagreements (e.g., using arbitration in red teaming), leveraging relationships between data points to estimate reliability (e.g., in news source evaluation), and accounting for both inter- and intra-annotator agreement to identify and mitigate inconsistencies. These advancements are vital for enhancing the performance and robustness of various machine learning applications, particularly in domains like natural language processing and semi-supervised learning, where reliable labels are often scarce or expensive to obtain.