(AI)DENTITY: Who Are We in the Age of Algorithms?

An interactive exploration of global AI perspectives based on research analyzing 59,542 voices across three continents.

The Website

We’ve fixed the deployment issue we had earlier—our project is now live and running smoothly on Vercel. You can explore the interactive site here: https://aidentity-story.vercel.app/

Quick Start

• This section is for anyone who wants to run the project locally or deploy it themselves.

Prerequisites

• Node.js (v16 or higher)
• npm or yarn

Installation & Setup

1. Clone the repository

   git clone https://github.com/yourusername/aidentity.git
   cd aidentity

2. Install dependencies

   npm install

3. Set up environment variables

   cp .env.example .env
   # Edit .env with your configuration

4. Start the development server

   npm run dev

5. Open your browser Navigate to http://localhost:3000

Features

• Personal Narrative: Starts with relatable stories and builds to global insights
• Interactive Quiz: Discover which of 5 AI personas matches your perspective
• Real Research Data: Based on actual survey responses from 59,542 participants
• Regional Analysis: Explore how geography shapes AI attitudes
• Responsive Design: Works seamlessly on desktop and mobile
• Data Visualizations: Dynamic charts showing persona distributions and fear profiles

The Five AI Personas

1. Balanced Social Participant (59.4%) - Cautiously optimistic, values human connection
2. Consistent Social Responder (22.2%) - Thoughtful engagement, social justice focus
3. Balanced Security Participant (3.4%) - Prioritizes safety and regulation
4. Cultural Preservationist (8.4%) - Concerned about tradition and values
5. Technology-Aware Participant (6.6%) - Understanding of risks while remaining engaged

Data Sources

This project analyzes survey data from:

• GD1: North American participants
• GD2: European participants
• GD3: Asia-Pacific participants

Total: 59,542 individual responses across 10 key dimensions of AI concern.

Technical Stack

• Frontend: React 18, Tailwind CSS, Recharts, D3.js
• Backend: Node.js, Express
• Data Processing: Custom algorithms for persona classification
• Deployment: Optimized for Vercel/Netlify

Project Structure

src/
├── components/          # Reusable UI components
├── data/               # Research data and constants
├── utils/              # Helper functions and calculations
└── styles/             # CSS and styling

backend/
├── routes/             # API endpoints
├── data/               # Raw and processed datasets
└── utils/              # Data processing utilities

Development

Available Scripts

• npm start - Run frontend only
• npm run server - Run backend only
• npm run dev - Run both frontend and backend
• npm run build - Build for production
• npm test - Run tests

Adding New Data

1. Place CSV files in backend/data/raw/
2. Run data processing: node backend/utils/dataProcessor.js
3. Update persona calculations in backend/utils/personaCalculator.js

API Endpoints

• GET /api/personas - Get all persona data
• POST /api/quiz/submit - Submit quiz responses and get persona match
• GET /api/data/regional - Get regional comparison data
• GET /api/data/distribution - Get persona distribution data

Customization

Styling

• Tailwind classes can be customized in tailwind.config.js
• Component-specific styles in individual component files

Data Visualization

• Chart configurations in src/components/DataVisualization.js
• Color schemes defined in src/data/personaData.js

Quiz Logic

• Questions in src/data/quizQuestions.js
• Scoring algorithm in src/utils/calculations.js

Deployment

Vercel (Recommended)

npm install -g vercel
vercel --prod

Netlify

npm run build
# Upload dist folder to Netlify

Traditional Hosting

npm run build
# Upload build folder to your hosting provider

Contributing

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

License

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

Acknowledgments

• Research participants who shared their perspectives
• Data visualization inspiration from The Pudding
• Open source community for tools and libraries