FairMind - Ethical AI Sandbox

Comprehensive ethical AI testing and governance platform with modern tooling (UV + Bun)

Project Overview

FairMind is a comprehensive ethical AI sandbox that provides 8 core features for testing, monitoring, and governing AI models with a focus on fairness, security, and ethical compliance.

Recent Achievements

• Complete Testing Infrastructure: 11 models (3 traditional + 8 LLM) tested
• Modern Tooling: UV (Python) + Bun (JavaScript) workflow
• 100% Feature Coverage: All 8 FairMind features validated
• Production Ready: Backend deployed to Railway, Frontend to Netlify

Quick Start

Prerequisites

# Install modern tooling
curl -LsSf https://astral.sh/uv/install.sh | sh  # UV for Python
curl -fsSL https://bun.sh/install | bash         # Bun for JavaScript

Backend Setup

cd apps/backend
uv sync                    # Install Python dependencies
uv run python -m uvicorn api.main:app --host 0.0.0.0 --port 8001 --reload

Frontend Setup

cd apps/frontend
bun install               # Install JavaScript dependencies
bun run dev               # Start development server

Testing Suite

# Run comprehensive testing
cd test_scripts
bun run setup             # Setup testing environment
python comprehensive_fairmind_test.py  # Test traditional ML
python llm_comprehensive_test.py       # Test LLM models

Core Features

Feature	Description	Status
Bias Detection	Comprehensive fairness analysis with 5 bias metrics	Tested
AI DNA Profiling	Model signatures and lineage tracking	Tested
AI Time Travel	Historical and future analysis capabilities	Tested
AI Circus	Comprehensive testing suite	Tested
OWASP AI Security	All 10 security categories	Tested
AI Ethics Observatory	Ethics framework assessment	Tested
AI Bill of Materials	Component tracking and compliance	Tested
Model Registry	Lifecycle management and governance	Tested

Testing Results

Models Tested: 11

• Traditional ML: 3 models (Healthcare, HR Analytics, Credit Risk)
• LLM Models: 8 models (GPT-2, BERT, DistilBERT, ResNet50/18, VGG16)
• Accuracy: >88% across all traditional models
• Success Rate: 100% for all downloads and tests

Test Coverage: 100%

• 24 Test Cases: All 8 features × 3 traditional models
• LLM Testing: Image classification bias analysis
• Security: All 10 OWASP AI categories
• Compliance: Complete AI BOM and governance testing

Architecture

fairmind-ethical-sandbox/
├── apps/
│   ├── backend/           # FastAPI backend (Railway deployed)
│   ├── frontend/          # Next.js frontend (Netlify deployed)
│   └── website/           # Astro documentation site
├── test_models/           # 11 trained/downloaded models
├── test_scripts/          # Comprehensive testing suite
├── test_results/          # Detailed test reports
└── docs/                  # Complete documentation

Technology Stack

Backend (Python + UV)

• Framework: FastAPI with Uvicorn
• ML Libraries: scikit-learn, pandas, numpy, xgboost
• LLM Libraries: transformers, torch, torchvision
• Testing: pytest, requests, comprehensive test suite

Frontend (JavaScript + Bun)

• Framework: Next.js 14 with React 18
• Styling: Tailwind CSS with custom terminal theme
• Testing: Axios, Chalk, Ora for CLI testing
• Build: Modern ES modules and async/await

Infrastructure

• Backend: Railway deployment (api.fairmind.xyz)
• Frontend: Netlify deployment (app-demo.fairmind.xyz)
• Testing: Automated UV + Bun workflow
• Documentation: GitHub Wiki and comprehensive docs

Performance Metrics

Metric	Target	Achieved	Status
Bias Detection Coverage	100%	100%	Complete
Security Coverage	100%	100%	Complete
Model Performance	>85%	>88%	Complete
Test Coverage	100%	100%	Complete
LLM Download Success	100%	100%	Complete
Documentation Quality	Professional	Professional	Complete

Deployment

Production URLs

• Backend API: https://api.fairmind.xyz
• Frontend App: https://app-demo.fairmind.xyz
• Documentation: https://fairmind.xyz

Development

# Backend (Port 8001)
cd apps/backend && uv run python -m uvicorn api.main:app --reload

# Frontend (Port 3000)
cd apps/frontend && bun run dev

# Testing
cd test_scripts && bun run setup

Documentation

• FINAL_TESTING_SUMMARY.md - Complete testing achievements
• TESTING_PLAN.md - Comprehensive testing strategy
• test_scripts/README.md - Testing documentation
• docs/ - Complete project documentation
• GitHub Wiki - User guides and tutorials

Contributing

Development Workflow

1. Main Branch: Production-ready code
2. Dev Branch: Active development
3. Testing: UV + Bun automated testing
4. Deployment: Railway + Netlify CI/CD

Testing Requirements

• All new features must pass comprehensive testing
• Maintain >88% model accuracy
• Ensure 100% security and bias detection coverage
• Update documentation for all changes

License

This project is licensed under the MIT License - see the LICENSE file for details.

Support

• Documentation: GitHub Wiki
• Issues: GitHub Issues
• Testing: Test Results
• Deployment: Production URLs

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FairMind is ready for real-world ethical AI testing.

Built with modern tooling (UV + Bun) for the future of ethical AI governance.