Dataset labeling UX and human-in-the-loop best practices

Questions for teams running annotation at scale: - Interface: What UI patterns reduced error and fatigue? - Quality: Calibration rounds and adjudication processes. - Incentives: Throughput vs quality trade-offs. - Feedback: Closing the loop to show impact of labels on models. - Tooling: Label Studio, Prodigy, custom – what scaled and integrated well? Share templates for guidelines and tips to prevent annotator drift.