Please note: The algorithm descriptions in English have been automatically translated. Errors may have been introduced in this process. For the original descriptions, go to the Dutch version of the Algorithm Register.
Text analysis
- Publication category
- Other algorithms
- Impact assessment
- Field not filled in.
- Status
- In use
General information
Theme
Begin date
Contact information
Link to publication website
Responsible use
Goal and impact
Support in the review process where legal protection applies to information that is disclosed. Protection from the AVG (persons) and Woo laws (especially company confidential), where grounds for exception are named.
Considerations
Manual review is intensive and error-prone. A suggestion list from the entity extraction algorithm captures all conceivable instances of individuals in the text.
Human intervention
Within the software, a list is built and offered to the user to select in the automatic varnishing process. The choice to adopt an advised term as a person name and not to disclose it is up to the user.
Risk management
There is no risk of automated decision-making and the algorithm has no impact on fundamental rights because the algorithm does not make decisions with legal consequences. It only makes a proposal for anonymising personal data. The employee of the administrative body always makes the final check whether a document has been correctly anonymised.
Legal basis
Legislation around public access to government data (Woo)
Links to legal bases
Operations
Data
This refers to documents and messaging information within central government. Including email, files, Whatsapp messages and other media where administrative decision-making can be found.
Links to data sources
Technical design
Texts are recognised on the basis of Named Entity Recognition (NER) and a process within Insights extracts the names for further processing towards the management interface and the automatic lacquer rules.
External provider
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