Zingg on permission design for human-AI shared decision-making
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The open-source entity resolution and master data management platform Zingg proposes that when AI agents and data stewards operate on the same records simultaneously, the system must clearly define what human decisions lock, how long they lock it, and who has the authority to reopen a decision after the underlying data changes. Zingg says that in the era of traditional rule-based matching, the system was merely an extension of the administrator, whereas probabilistic machine learning gives the matching engine its own judgment, which may be confident yet wrong. Zingg is building data stewardship mechanisms into the platform to handle this kind of human-AI conflict.
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- Hacker News AIA Human Corrected the AI. Then the Data Changed
Zingg describes how adding a simple steward approval flag to its incremental entity resolution and MDM platform raised harder questions about what a human decision locks in, how long it holds, and who may reopen it when underlying data changes. The post lays out design principles (members stay put, no automatic merges, growth allowed), walks through concrete edge cases such as a father-and-son household split, and argues that the system being corrected should not control when a human decision expires. It closes with general guidance for any system where agents and people share decisions over time.
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