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nguyenthanhasia/gdpr-cases-demo

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GDPR Cases - Interactive Demo

An interactive Streamlit application for exploring GDPR formalization cases and understanding step-by-step rule evaluation using the Pythen framework.

Features

  • —Dataset Browsing: Browse all 60 verified GDPR cases from the dataset
  • —Sample Selection: Choose any sample by article and ID
  • —Scenario Viewing: Read the complete legal scenario for each case
  • —Facts Display: View extracted atomic facts used in evaluation
  • —Rule Tree Visualization: Inspect the formal rule tree in JSON format
  • —Quality Metrics: See evaluation scores from multiple verifiers
  • —Step-by-Step Evaluation: Understand how Pythen evaluates rules
  • —Ground Truth: View the expected legal outcome for each case

Dataset

This demo uses the GDPR Cases dataset (nguyenthanhasia/gdpr-cases):

  • —60 verified samples of GDPR formalization cases
  • —11 columns including scenario, rule tree, facts, and evaluation scores
  • —High quality: All samples verified by legal experts

How It Works

Rule Evaluation Process

  1. 1.Parse Rule Tree: Hierarchical structure of conditions and predicates
  2. 2.Extract Facts: Atomic facts from the legal scenario
  3. 3.Traverse Tree: Evaluate from root through all nodes
  4. 4.Apply Operators:
  5. 5.ANY: At least one condition must be true
  6. 6.ALL: All conditions must be true
  7. 7.Derive Label: Final boolean result (TRUE/FALSE)

Pythen Framework

Pythen is a formal representation framework for legal rules that:

  • —Separates conditions from exceptions
  • —Uses logical operators (ANY, ALL) for composition
  • —Enables automated evaluation of legal provisions
  • —Supports complex nested rule structures

Citation

If you use this demo or dataset, please cite:

bibtex
@article{nguyen2026gdpr,
  title={GDPR Auto-Formalization with AI Agents and Human Verification},
  author={Nguyen, Ha Thanh and Fungwacharakorn, Wachara and Wehnert, Sabine and Zin, May Myo and Kong, Yuntao and Xue, Jieying and Araszkiewicz, Michał and Goebel, Randy and Satoh, Ken},
  journal={arXiv preprint arXiv:2604.14607},
  year={2026}
}

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