When Data Changes After Human Correction: Zingg Reviews System Design Boundaries for Human-in-the-Loop Decision-Making

Open-source entity resolution platform Zingg has reviewed the system design challenges of human-in-the-loop decision-making, pointing out that when underlying data changes after human corrections to AI, the system must clearly define the lock scope and permission boundaries of human decisions. The team proposed that while systems may add new records to human-verified entity clusters, they must strictly prohibit automatic splitting or merging, treating grouping relationships rather than individual data records as the unit of approval. The article emphasized that model confidence scores must never dictate when human decisions expire; instead, triggers for re-review must be governed by external rules or business events.