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 and document varnishing
- Publication category
- Other algorithms
- Impact assessment
- DPIA
- Status
- In use
General information
Theme
Begin date
Contact information
Responsible use
Goal and impact
Support in the review process where legal protections apply to information 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 Province employee always does the final check whether a document is correctly anonymised.
Legal basis
Legislation around public access to government data (Woo)
Links to legal bases
Impact assessment
Operations
Data
This refers to documents and messaging information within the Province. 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
Similar algorithm descriptions
- Based on language technology, personal and company names are read and filtered out of text files such as emails and individual documents.Last change on 14th of October 2024, at 10:47 (CET) | Publication Standard 1.0
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- The algorithm underlines personal data in documents. An employee has to look at all pages and check if the document is properly lacquered. Then the software removes all highlighted information and blacklists it. After that, the documents can be published, for example under the Open Government Act (WOO).Last change on 5th of February 2025, at 9:15 (CET) | Publication Standard 1.0
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- The algorithm recognises (personal) data and otherwise confidential information in a document and makes a proposal to anonymise it. A staff member evaluates the proposal and makes the final adjustment, making the document suitable for publication.Last change on 25th of January 2024, at 12:18 (CET) | Publication Standard 1.0
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- The algorithm recognises (personal) data and otherwise confidential information in a document and makes a proposal to anonymise it. A staff member evaluates the proposal and makes the final adjustment, making the document suitable for publication.Last change on 7th of October 2024, at 15:33 (CET) | Publication Standard 1.0
- Publication category
- Other algorithms
- Impact assessment
- Field not filled in.
- Status
- In use