Choose a transparent allocation model for human work.
Assignment design balances fairness, specialization, throughput, review independence, and operational control. The queue should express that design visibly.
Unitlab supports direct assignment and “Anyone” availability. A mature operating model combines:
pooled work for routine tasks;
direct assignment for accountable ownership;
expert routing for difficult cases;
priority values for urgent or high-value units;
reviewer independence where the risk justifies it.
Assignment belongs to the workflow item state. Workspace role determines broad authority; project position and stage configuration determine whether a user can work as an annotator or reviewer in that queue.
Use self-claim for broad eligible pools and manual assignment for specialization or controlled calibration.
Keep reviewer independence and conflict rules explicit.
Define priority inputs, tie-breaking, aging, and starvation prevention.
Monitor unassigned, long-running, repeatedly rejected, and reopened cohorts.
Change the allocation rule through workflow and role review—not through side-channel spreadsheets.
Related Unitlab capability guides: AI training-data annotation
