RegelRecht

RegelRecht is an exploration by the Dutch Ministry of the Interior (Bureau Architectuur) into machine-executable legislation. The project investigates how we can achieve transparent, unambiguous, and consistent execution of laws - enabling everyone to understand how decisions are made.

This initiative explores whether laws can be written as directly executable code, eliminating the gap between legislation and implementation. By creating machine-readable legal specifications, RegelRecht aims to:

  • Provide one single source of truth for legal rules that all parties use
  • Enable full transparency and traceability of government decisions
  • Test new legislation before implementation to detect conflicts and inconsistencies
  • Reduce interpretation differences across government organizations

The ecosystem includes NRML (Normalized Rule Model Language) as a JSON-based format for machine-executable laws, execution engines in multiple programming languages, an AI-powered converter for existing analog law, a visual law editor, and simulation environments for testing legislative impact.

Learn more at minbzk.github.io/regelrecht or explore the source code on GitHub.

Algorithm Management Toolkit (AMT)

A comprehensive platform for the governance and oversight of algorithmic systems within organizations. Developed for the Dutch government, AMT provides a structured approach to documenting, testing, and managing both AI and non-AI algorithms used in public services and decision-making processes.

The toolkit features a bookkeeping system for algorithmic applications, technical validation tools, ethical assessment frameworks, and transparency reporting capabilities. It helps organizations maintain proper documentation, ensure regulatory compliance, and implement responsible AI practices. AMT represents an important step toward algorithmic accountability in governance and public service delivery.

The source code is available on GitHub.

Living Labs

The Living Labs for IR Evaluation (LL4IR) is a new evaluation paradigm. I implemented an API for participants ( researchers) and sites (search engines) that take part in this Living Lab (which is also run as a CLEF lab). The API allows participants (researchers) to evaluate their ranking systems on real users of real sites (search engines). On the flip site, it allows sites (search engines) to benefit from the knowledge of the research community.

The LL4IR API can be used by researchers to perform several actions such as obtaining queries, documents and feedback and to update runs. The API is RESTful, that is, everything is implemented as HTTP request, and we use the request types GET, PUT and DELETE.

The source code is available from bitbucket.

It has mainly been developed by Anne Schuth and Krisztian Balog.

Related publications:

Lerot: an Online Learning to Rank Framework

Lerot is a framework, designed to run experiments on online learning to rank methods for information retrieval. It has mainly been developed by Katja Hofmann and Anne Schuth. The source code of Lerot is available from bitbucket. A paper describing Lerot is published in the Living Labs Workshop at CIKM’13: