FrontierAI-Safety A Comprehensive Framework for Evaluating Safety in Frontier AI Models

📜 Overview The FrontierAI-Safety project focuses on evaluating the safety and ethical robustness of advanced AI models, particularly in the context of:

Adversarial misuse resistance Ethical dilemma handling Bias in emergent reasoning capabilities Detection of high-stakes deception By applying structured testing methodologies and leveraging cutting-edge frameworks, this repository aims to strengthen the safeguards around Frontier AI systems.

🚀 Features Adversarial Testing Scenarios: Multi-step prompts designed to probe ethical and safety vulnerabilities. Ethical Evaluation Metrics: Quantifiable metrics like the Composite Ethical Risk Metric (CERM). Automated Safety Evaluation: Scripts for testing adversarial resistance and reasoning stability. Documentation in LaTeX: Well-organized documentation to ensure clarity and reproducibility. Conceptual Frameworks: Diagrams and theoretical models for ethical risk analysis. 📂 Repository Structure graphql Copy code FrontierAI-Safety/ ├── src/ # Source code for safety evaluation scripts ├── docs/ # Documentation and LaTeX files ├── tests/ # Adversarial scenarios and test cases ├── figures/ # Diagrams and visual assets ├── data/ # Datasets (if applicable) ├── references/ # Related papers, articles, and citations ├── LICENSE # License for the project ├── README.md # Overview of the repository └── .gitignore # Ignored files and folders 🔧 Installation To clone this repository and set up the environment:

Clone the repository: bash Copy code git clone https://github.com/yourusername/FrontierAI-Safety.git cd FrontierAI-Safety Install the required dependencies: bash Copy code pip install -r requirements.txt Compile the LaTeX documentation (optional): bash Copy code cd docs/ pdflatex main.tex 🛠 Usage Run Basic Tests: bash Copy code python src/run_basic_tests.py Execute Adversarial Scenarios: bash Copy code python src/adversarial_tests.py Generate Metrics: bash Copy code python src/evaluation_metrics.py View the documentation for detailed test cases and conceptual frameworks in the docs/ folder. 📊 Evaluation Metrics We use the Composite Ethical Risk Metric (CERM) to assess the model's safety performance. This metric considers:

Probability of harmful outputs Bias indicators in decision-making Disclosure of unintended harmful knowledge For details, refer to the Metrics Documentation.

📖 Documentation Comprehensive documentation is available in the docs/ directory. It includes:

Evaluation frameworks Mathematical models for ethical testing Step-by-step instructions for reproducing results 🤝 Contributing We welcome contributions! Please see the CONTRIBUTING.md file for guidelines.

How to Contribute: Fork the repository. Create a new branch (git checkout -b feature/new-feature). Commit your changes (git commit -m "Add new feature"). Push to the branch (git push origin feature/new-feature). Open a pull request. 📄 License This project is licensed under the MIT License.

💬 Contact For questions or suggestions, feel free to reach out:

Name: Daniel Aguirre Email: your.email@example.com GitHub: github.com/yourusername ⭐ Acknowledgments OpenAI, Google, and Anthropic for their groundbreaking contributions to AI research. The broader AI research community for inspiring this project.