Encord Active is an open-source active learning framework that makes it easy to find and fix label errors in computer vision datasets. It uses a user-friendly UI and visualizations to help investigate and understand failure modes in models, and can quickly identify label errors with just a few clicks. The framework helps improve model performance by finding incorrect labels in training data, which can significantly impact the performance of computer vision models. Encord Active identifies three types of labeling errors: mislabeled objects, missing labels, and inaccurate labels. It provides a 4-step approach to find label errors using pre-trained models: run a model on newly annotated samples, visualize predictions and ground truth labels, sort for high-confidence false positive predictions, and flag missing or wrong labels. The framework can also be used to fix label errors with its user-friendly UI and label editor.