Submit a case for review by a normative advice commission

Cases can be submitted anonymously. Read more about how we work. You can find more information below about next steps after case submission.

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Submitted data will only be processed for the purpose described above, kept for the minimum necessary duration, and is securely stored in a protected environment*

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Add a case to algoprudence repository

Would you like to contribute to our algoprudence repository? Please complete the below form. Please familiarize yourself with the cases already present in the repository. You can find more information below about next steps after case submission.

Case information

Relevant documents

Terms and conditions
Submitted data will only be processed for the purpose described above, kept for the minimum necessary duration, and is securely stored in a protected environment*

* required

What are the next steps after case submission?

Your submission is reviewed by Team Algoprudence. The team assesses whether de case aligns with the mission of Algorithm Audit and the availability of resources for further investigation. Notification regarding any follow-up steps can be expected within two weeks.

Team Algoprudence

Vardâyani Djwalapersad

Project manager Algoprudence

Jurriaan Parie

Director-board member

Ariën Voogt

Board member

Samaa Mohammad-Ulenberg

Board member

Recent audits

Risk Profiling Social Welfare Re-examination

Normative advice commission provides rationales why these variables are eligible or not as a profiling selection criterion for a xgboost algorithm

Technical audit indirect discrimination

Qualitative and quantitative audit of algorithmic-driven college allowances control process. Assessment of risk distributions through Z-tests and bias test in the control process

Risk Profiling Social Welfare Re-examination

Normative advice commission provides rationales why these variables are eligible or not as a profiling selection criterion for a xgboost algorithm

Technical audit indirect discrimination

Qualitative and quantitative audit of algorithmic-driven college allowances control process. Assessment of risk distributions through Z-tests and bias test in the control process

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