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.
Zylab Disclosure Support
- 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
The algorithm helps employees review documents quickly and properly. This is done when disclosing information. With smart search and filter functions, reviewers can better see which documents are important for a request. As a result, requests can be handled faster and better. Citizens and businesses get their questions answered faster.
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
The algorithm does not make its own decisions. Humans evaluate all results of searches, groups and finding duplicate documents. A human always gives the final result per document. If there is any doubt, a second reviewer or team leader looks at the document again.
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
The basis for the processes is as follows: 1. For disclosure: the Open Government Act (Woo). 2. For inspection: the General Data Protection Regulation (AVG). 3. For objection and appeal: the General Administrative Law Act (Awb). 4. For parliamentary enquiries: the Parliamentary Inquiry Act 2008 (WPE).
Links to legal bases
Link to Processing Index
Operations
Data
The algorithm processes documents associated with the request. These can be e-mails, such as messages and attachments, as well as office documents such as Word, Excel, PowerPoint and PDF. In addition, chat messages and structured data from internal systems can be processed. These documents may contain personal data, such as names, e-mail addresses, phone numbers and job titles of employees, citizens and others. Metadata can also be used, such as creation date, author, file type and file name.
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 automatic paint rules.
External provider
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