Параллельное сравнение Data Provenance и Data Source Register. Объясняет, чем отличаются понятия, когда применяется каждый термин и почему различие важно для AI governance, оценки и проектирования систем.
Краткий вердикт: Используйте data provenance для истории происхождения и изменений данных, а data source register — для управляемого реестра источников, используемых в AI workflows.
Data Provenance describes recorded history of data, including its origin, creation, transformations, movements, and changes over time.
Контекст: Most relevant, когда documenting, evaluating, or governing сценарии применения where Data Provenance needs to be distinguished from Data Source Register.
Data Source Register summarizes record of the datasets and sources used in an AI workflow, including training, fine-tuning, evaluation, and retrieval-augmented generation.
Контекст: Most relevant, когда documenting, evaluating, or governing сценарии применения where Data Source Register needs to be distinguished from Data Provenance.
| Аспект | Data Provenance | Data Source Register |
|---|---|---|
| Цель | Используйте Data Provenance, когда the governance record, assurance activity, or oversight workflow matches this definition и evidence type. | Используйте Data Source Register, когда the governance record, assurance activity, or oversight workflow matches this definition и evidence type. |
| Владелец | Ownership usually belongs to the team or role accountable для the Data Provenance activity, record, or decision. | Ownership usually belongs to the team or role accountable для the Data Source Register activity, record, or decision. |
| Входные данные | Inputs include the data, system facts, criteria, и records needed to apply Data Provenance consistently. | Inputs include the data, system facts, criteria, и records needed to apply Data Source Register consistently. |
| Выходы | Outputs следует be reviewable records, decisions, or evidence showing how Data Provenance was applied. | Outputs следует be reviewable records, decisions, or evidence showing how Data Source Register was applied. |
| Audit trail | The audit trail следует show, когда Data Provenance was assessed, by whom, against what criteria, и with what supporting evidence. | The audit trail следует show, когда Data Source Register was assessed, by whom, against what criteria, и with what supporting evidence. |
На практике, Data Provenance и Data Source Register are strongest, когда linked to owners, artifacts, review dates, и evidence that может survive audit scrutiny.
Using Data Provenance и Data Source Register as synonyms even though they answer different governance or technical questions.
Documenting the term without the context, system boundary, dataset, actor, or lifecycle stage that makes it applicable.
Relying on the label alone instead of preserving evidence that supports the classification.
Используйте Data Provenance, когда you need to describe recorded history of data, including its origin, creation, transformations, movements, и changes over time. In governance-документации, connect it to the relevant owner, lifecycle stage, evidence, и controls so the term is not used as a loose label.
Используйте Data Source Register, когда you need to describe record of the datasets и sources used in an AI workflow, including training, fine-tuning, оценки, и retrieval-augmented gene.... In governance-документации, connect it to the relevant owner, lifecycle stage, evidence, и controls so the term is not used as a loose label.
The comparison helps teams build repeatable governance processes with clear owners, records, и review checkpoints. In ISO/IEC 42001 и NIST AI RMF style governance, the distinction helps connect риски, controls, owners, и monitoring evidence.
Data Provenance определяется через recorded history of data, including its origin, creation, transformations, movements, и changes over time. Data Source Register определяется через record of the datasets и sources used in an AI workflow, including training, fine-tuning, оценки, и retrieval-augmented gene.... The practical difference is the scope, evidence, и decision context attached to each term.
Да, they может both appear in the same AI project, когда their definitions match different parts of the system, lifecycle, or governance record. They следует still be documented separately so responsibilities и controls remain clear.
Используйте the term that matches the specific fact pattern you are documenting. If the record concerns both Data Provenance и Data Source Register, define each one explicitly и connect it to the relevant owner, evidence, и control.
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