Example Concept: Consensus labeling involves having multiple annotators label the same data points and using a majority vote or weighted agreement to determine the final label. This approach helps reduce individual biases and errors. Active learning can be used to iteratively select the most informative samples for labeling, ensuring that annotators focus on ambiguous or uncertain instances that are more prone to noise. Implementing quality control checks, such as spot-checking annotations and using gold-standard datasets, further ensures the accuracy and consistency of the labels.