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.
Metrology KW
- Publication category
- Impactful algorithms
- Impact assessment
- IAMA
- Status
- In use
General information
Theme
Begin date
Contact information
Link to publication website
Responsible use
Goal and impact
The aim of the algorithm is to enable risk-based supervision of the Metrology Act for the small weighing equipment category (e.g. weighing scales in supermarkets). The algorithm selects companies where there is an increased risk of violation of the Metrology Act. Inspectors' available time can be used more effectively this way.
Considerations
It is not possible to visit all companies covered by the Metrology Act every year. The inspector therefore has to weigh up which locations to visit. This consideration is based on knowledge and experience, however, this is time-consuming and, in addition, experience is not directly transferable to new employees. The algorithm can quickly and independently, based on a large amount of information, suggest which locations could be inspected.
Human intervention
The outcome of the algorithm is a list of companies with an increased risk of violating the Metrology Act. Prior to incorporating this list into the inspections to be scheduled, it is reviewed by the planner in cooperation with the process manager. Inspectors who receive an individual schedule do not know which inspections have been identified by the algorithm. However, they do have discretion, based on expertise and working conditions, to make final changes to their planning.
Risk management
Due to possible statistical patterns in historical inspections, the algorithm may exhibit bias in risk estimates, as much of the data used for training comes from these historical inspections. To mitigate this risk, the following measures have been implemented:
1: 50% of all inspections are performed randomly, i.e. not based on the algorithm.
2: To avoid overrepresentation of specific chains in the list compiled by the algorithm, limits are placed on the number of times sites from one chain may appear in this list.
3: The list of sites selected by the model is reviewed before being incorporated into planning.
4: A review of the inspections carried out is done annually. The algorithm is then re-trained.
Legal basis
metrology law
Links to legal bases
Elaboration on impact assessments
An IAMA helps ensure that the algorithm is not only efficient but also used ethically.
The IAMA discussed the following issues.
Discrimination: Does the algorithm ensure a fair selection of companies, without unwanted biases based on e.g. postcode or chain?
Transparency: Is it clear to involved parties how decisions are made and on asis of what?
Privacy: Are the data of companies and individuals processed carefully and according to legislation?
Accountability: Who bears responsibility in case of errors or undesirable outcomes? The following conclusions were drawn from the IAMA:
1) By using the algorithm, the goal is achieved
2) By using the algorithm, a person's fundamental rights can only be indirectly affected, making the interference with the fundamental right minimal and explainable.
3) There is a balance between achieving the goal and the impairment in fundamental rights
4) By using the algorithm, more consideration is given to available resources
5) Using the algorithm increases objectivity
6) There is no residual damage
Impact assessment
Operations
Data
Three sources of information were used by the algorithm. The first source is the internal Locatus database which lists all companies covered by the Metrology Act, this contains the company name, industry, postcode, chain (yes/no), and name of any chain. The second source is the internal VIS database which records the results of all historical inspections. As a third source, CBS was used to determine urbanity and average density based on postcode (PC-4).
Links to data sources
Technical design
The algorithm works on the basis of logistic regression. Logistic regression is a method that helps predict whether or not something will happen. The algorithm uses data (features) such as company name, industry, postcode and chain. This information is analysed to calculate a probability of violating or not violating the metrology law (the target).
The model works by finding relationships between the features and the target. Based on these calculations, the model determines which companies have an increased risk.
External provider
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