Algorithm Register Template

All fields with example texts and practical tips

This template helps you correctly and completely fill in the Algorithm Register. For each field you will find an explanation, example text and points of attention.

Basic Information

Name of the algorithm

Required field

A clear, recognizable name for the algorithm.

Example text (fictional):

"Fraud Risk Detection Benefits"

Avoid abbreviations or internal codes · Make the name understandable for citizens

Short description

Required field

A brief explanation of what the algorithm does (max. 500 characters).

Example text (fictional):

"This algorithm analyzes applications for social benefits to identify signals that may indicate possible irregularities. The system provides a risk score used by staff when determining follow-up investigation."

Avoid jargon · Explain WHAT it does, not HOW it technically works · Be honest about the purpose

Status

Required field

The current status of the algorithm.

Example text (fictional):

"Operational"

Choose from: In development / In pilot / Operational / Temporarily out of use / Phased out

Ownership

Organization

Required field

The responsible government organization.

Example text (fictional):

"Municipality of Sampletown, Department of Work and Income"

Also mention the specific department or directorate

Contact person/department

Required field

Where can citizens or journalists go with questions?

Example text (fictional):

"Information Department - algorithms@amsterdam.nl"

Preferably use a functional email address · Not personal details of an employee

Application

Policy area

Required field

In which policy area is the algorithm deployed?

Example text (fictional):

"Social security and employment"

Use common policy areas that citizens recognize

Target group

Required field

Which group is assessed or affected by the algorithm?

Example text (fictional):

"Residents applying for or receiving social benefits"

Be specific about who is affected · Also mention potentially vulnerable groups

Purpose of deployment

Required field

What is the intended result of using this algorithm?

Example text (fictional):

"More efficient deployment of enforcement capacity by identifying applications that may require extra attention."

Be transparent about the actual purpose · Avoid euphemistic language

Data & Technology

Data used

Required field

Which data sources and data types are used?

Example text (fictional):

"Personal data from population register, income data (UWV), debt registration (BKR), historical benefit data"

Name all data sources explicitly · State whether personal data is processed · Be complete - incomplete information undermines trust

Type of algorithm

Required field

Is it a rule-based system or a learning system (machine learning)?

Example text (fictional):

"Machine learning - classification model trained on historical files"

Rule-based = fixed rules, transparent logic · Machine learning = learns from data, less transparent

Supplier

Is the system developed internally or supplied by an external party?

Example text (fictional):

"Developed in-house by the municipal data team, with guidance from a research institution"

Also mention collaboration partners · For external supplier: name them

Human Control & Decision-making

Role of algorithm in decision-making

Required field

How does the algorithm output relate to the final decision?

Example text (fictional):

"The algorithm generates a risk score and signaling list. Staff assess each signal individually before taking follow-up steps. The algorithm does not make independent decisions."

Clearly explain whether human assessment always takes place · Be honest if human control is limited

Possibility of objection

Required field

Can citizens object if they are affected by the algorithm?

Example text (fictional):

"Citizens are informed when their file is subject to additional investigation. They can object through the regular objection procedure against decisions."

Explain how citizens can challenge algorithmic decisions

Risk Assessment

Assessments conducted

Which risk assessments have been conducted?

Example text (fictional):

"DPIA conducted in March 2026, IAMA conducted in June 2026, bias audit by an external firm in September 2026"

Name all conducted assessments with date · Link to published reports if possible

AI Act risk classification

What is the classification under the EU AI Act?

Example text (fictional):

"High-risk (Annex III, point 5(a): access to public assistance benefits and services). The obligations for Annex III high-risk systems apply from 2 December 2027."

Use the Annex III classification check to determine this · State your role: usually deployer

Publication & Review

Publication date

Required field

When was this registration published?

Example text (fictional):

"January 15, 2025"

Review cycle

How often is this registration updated?

Example text (fictional):

"Annually, and upon significant changes to the system"

Schedule periodic review · Update upon changes

Editable in Word and Excel

The public sector pack contains the AI procurement checklist for public bodies, the FRIA template, the DPIA template for AI and the AI register with classification, with the law as of 6 October 2026. The pack is sent to your business email address.

FRIA template (Article 27) · AI procurement checklist for public bodies · Annex III classification check

Disclaimer: This template is based on the Algorithm Register format of the Dutch Government. Always check current requirements at algoritmes.overheid.nl.

Based on the national Algorithm Register format

As of 6 October 2026