Values in the Wild: Implementation and Analysis Framework

A comprehensive toolkit for implementing, analyzing, and validating AI value alignment based on Anthropic's "Values in the Wild" research.

Architecture

graph TD
    subgraph Core["Core Framework"]
        Extract[Extraction Module]
        Taxonomy[Taxonomy Module]
        Anon[Anonymization Module]
        Simulate[Simulation Module]
        Analyze[Analysis Module]
    end

    subgraph Data["Data Resources"]
        ValueData[Value Taxonomies]
        Samples[Chat Samples]
        Frequencies[Value Frequencies]
    end

    subgraph Workflows["Workflows"]
        Extract_Flow[Value Extraction]
        Analysis_Flow[Distribution Analysis] 
        Simulation_Flow[Chat Simulation]
        Anonymization_Flow[Privacy-Preserving Anonymization]
    end

    %% Core Module Relationships
    Extract --> Taxonomy
    Simulate --> Taxonomy
    Analyze --> Taxonomy
    
    %% Data Relationships
    ValueData --> Taxonomy
    Frequencies --> Simulate
    Samples --> Extract
    Extract --> Samples
    
    %% Workflow Relationships
    Extract_Flow --> Extract
    Extract_Flow --> Anon
    Analysis_Flow --> Analyze
    Simulation_Flow --> Simulate
    Anonymization_Flow --> Anon
    
    %% Privacy Layer
    Anon -.-> Extract
    Anon -.-> Analyze
    
    classDef core fill:#f9f,stroke:#333,stroke-width:2px
    classDef data fill:#bbf,stroke:#333,stroke-width:1px
    classDef flow fill:#bfb,stroke:#333,stroke-width:1px
    
    class Extract,Taxonomy,Anon,Simulate,Analyze core
    class ValueData,Samples,Frequencies data
    class Extract_Flow,Analysis_Flow,Simulation_Flow,Anonymization_Flow flow

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Environment Setup

This project uses uv for Python dependency management and make for workflow automation.

Prerequisites

• Python 3.9+
• uv (Python package manager)
• Make

Setup Workflow

sequenceDiagram
    participant User
    participant Make
    participant UV
    participant Python
    
    User->>Make: make setup
    Make->>UV: uv venv .venv
    UV-->>Make: Virtual environment created
    Make->>UV: uv pip install -r requirements.txt
    UV-->>Make: Dependencies installed
    Make-->>User: Setup complete
    
    User->>Make: make activate
    Make-->>User: Environment activation instructions
    
    User->>Make: make download-all
    Make->>Python: Download papers and datasets
    Python-->>Make: Downloads complete
    Make-->>User: Resources ready

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Getting Started

1. Clone the repository:

   git clone https://github.com/defrecord/value-alignment-toolkit.git
   cd value-alignment-toolkit

2. Set up the environment:

   make setup

   This will create a virtual environment using uv and install all dependencies.

3. Activate the environment:

   source .venv/bin/activate  # or use 'make activate' for instructions

4. Download required resources:

   make download-all

5. Run a sample analysis:

   make sample-analysis

Project Structure

• src/: Core implementation modules

  • extraction/: Value extraction algorithms
  • simulation/: Chat system simulation
  • anonymization/: Privacy-preserving techniques
  • analysis/: Statistical tools and visualizations
  • taxonomy/: Value hierarchy implementation

• data/: Data resources and outputs

  • values/: Reference data including value frequencies and taxonomies
  • samples/: Generated and anonymized conversation datasets

• tools/: Utility scripts

  • download/: Scripts to fetch relevant research papers and resources
  • validation/: Tools for testing and validating the implementation

• docs/: Documentation

  • tutorials/: Implementation guides and usage examples
  • paper/: Summaries of research methodology and key findings

Available Commands

Run make help to see all available commands.

License

[Appropriate license information]

Acknowledgments

This work builds upon research by Anthropic's "Values in the Wild" paper authored by Saffron Huang, Esin Durmus, et al.