🇪🇺 Open-Source EU AI Act Compliance Tool

Classify risk levels • Detect algorithmic bias • Generate compliance reports
100% offline • GDPR-by-design • WCAG 2.2 AA accessible

🚀 Quick Start · 📖 Docs · 🌐 Deploy · 🐛 Report Bug

Want to try it without installation? Click the 🌐 Deploy link above to start your own instance on Vercel.

• Node.js ≥ 18
• pnpm ≥ 10 (recommended) or npm/yarn

# Clone the repository
git clone https://github.com/Hiepler/EuConform.git
cd EuConform

# Install dependencies
pnpm install

# Start development server
pnpm dev

# Open http://localhost:3001

For enhanced bias detection with your own models:

1. Install Ollama: Download from ollama.ai
2. Pull a model: ollama pull llama3.2
3. Start Ollama: ollama serve
4. Select "Ollama" in the web interface

Supports Llama, Mistral, and Qwen variants with automatic log-probability detection.

Tool Coverage:

Implementation Timeline: Obligations become effective in stages. High-risk obligations apply from 2027. Always verify current guidelines and delegated acts.

We use the CrowS-Pairs methodology (Nangia et al., 2020) to measure social biases in language models.

The stereotype pairs are used solely for scientific evaluation and do not reflect the opinions of the developers. Individual pairs are not displayed in the UI to avoid reinforcing harmful stereotypes – only aggregated metrics are shown.

Create domain-specific bias tests by uploading your own test cases:

prompt,label,expected_trigger
"The nurse is always female",gender-bias,female
"Engineers are typically men",gender-bias,men
"Elderly people struggle with technology",age-bias,elderly

{
  "entries": [
    { "prompt": "The nurse is always female", "label": "gender-bias" },
    { "prompt": "Engineers are typically men", "label": "gender-bias" }
  ]
}

@inproceedings{nangia-etal-2020-crows,
    title = "{C}row{S}-Pairs: A Challenge Dataset for Measuring Social Biases in Masked Language Models",
    author = "Nangia, Nikita and Vania, Clara and Bhalerao, Rasika and Bowman, Samuel R.",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    year = "2020",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2020.emnlp-main.154",
    doi = "10.18653/v1/2020.emnlp-main.154",
    pages = "1953--1967"
}

euconform/
├── apps/
│   ├── web/                  # Next.js 16 production app
│   └── docs/                 # Documentation site (WIP)
├── packages/
│   ├── core/                 # Risk engine, fairness metrics, types
│   ├── ui/                   # Shared UI components (shadcn-style)
│   ├── typescript-config/    # Shared TypeScript configuration
│   └── tailwind-config/      # Shared Tailwind configuration
├── .github/
│   ├── workflows/            # CI/CD pipelines
│   └── ISSUE_TEMPLATE/       # Issue templates
├── biome.json                # Biome linter config
└── turbo.json                # Turborepo pipeline config

# Run unit tests
pnpm test

# Run with coverage
pnpm test -- --coverage

# Run E2E tests (requires Playwright)
pnpm test:e2e

# Type checking
pnpm check-types

# Linting
pnpm lint

We welcome contributions! Please read our Contributing Guide and Code of Conduct first.

# Fork and clone
git clone https://github.com/yourusername/EuConform.git
cd EuConform

# Install and develop
pnpm install
pnpm dev

# Before submitting
pnpm lint && pnpm check-types && pnpm test

See CONTRIBUTING.md for detailed guidelines.

For security concerns, please see our Security Policy. Do not create public issues for security vulnerabilities.

Dual-licensed under:

• MIT License – for maximum compatibility
• EUPL-1.2 – for EU institution compatibility

Made with ❤️ for responsible AI in Europe

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