About • Contents • Roadmap • Contributing
If you are also cautiously optimistic about AI ethics and safety, and feel a sense of mission towards ensuring AI models possess values of benevolence and fairness, join us! Let's build an amazing AI safety and ethics community together.
This repository serves as a comprehensive collection of resources focused on Large Language Model (LLM) ethics and safety. Our goal is to foster a community that promotes responsible AI development while ensuring these powerful technologies align with human values.
- Alignment & Value Learning
- Robustness & Safety Testing
- Transparency & Interpretability
- Ethical Guidelines & Governance
- Bias Detection & Mitigation
- Privacy & Security
🔮 Unpacking the Ethical Value Alignment in Big Models
More Resources
- Machine Learning & Deep Learning basics
- Neural networks & language models (GPT, BERT)
Recommended Resources:
- 📖 "Deep Learning" by Ian Goodfellow et al.
- 🎓 Andrew Ng's Machine Learning (Coursera)
- 💻 Hugging Face Free Tutorials
| Area | Description |
|---|---|
| Alignment | Ensuring AI goals align with human values |
| Explainability | Making AI decisions transparent |
| Robustness | Maintaining reliability under uncertainty |
| Control | Preventing AI systems from going rogue |
| Adversarial Attacks | Understanding various attack methods (adversarial samples, jailbreaking, prompt injection) and how they exploit model vulnerabilities |
| Safety Assessment | Designing effective safety evaluation methods and testing benchmarks (red team testing, fuzzing) to measure model security |
| Control Problem | Developing methods and frameworks to ensure AI systems remain under human control and operate within defined boundaries |
- Bias detection & correction
- Model output verification
- Adversarial attack defense
- Data bias detection and correction methodologies
- Model output reliability assessment
- Adversarial attack and defense strategies
| Tool | Purpose |
|---|---|
| OpenAI API | Safety practices and implementation |
| Hugging Face | Model safety toolkits and libraries |
| LIME & SHAP | Model interpretability analysis |
- Study AI ethics policy frameworks
- Understand key principles:
- Privacy
- Transparency
- Fairness
- Accountability
- Review international AI regulations:
- EU AI Act
- China's Generative AI Regulations
- Other regional AI policies
- Reproduce existing attack/defense methods
- Participate in open-source projects
- Join AI safety competitions and challenges
🎯 3.1Theoretical Innovation & Practical Contributions
- Pushing boundaries in both theory and implementation
- Contributing to cutting-edge research in the field
🔬 3.2Advanced Alignment Research
- Investigating state-of-the-art alignment techniques:
- Inverse Reinforcement Learning (IRL)
- Reinforcement Learning from Human Feedback (RLHF)
- Model Distillation & Value Learning Methods
- Studying research papers from leading institutions:
- OpenAI
- DeepMind
- Anthropic
🧠 3.3Value Systems Modeling
- Formalizing and embedding human values into AI systems
- Key research directions:
- Modeling diverse human values
- Culturally-Aware AI design
- Integration of ethical frameworks with decision theory
📊 3.4Research Objectives
| Area | Goals |
|---|---|
| Theory | Develop novel alignment approaches |
| Implementation | Create practical tools and frameworks |
| Validation | Design new evaluation metrics |
| Integration | Bridge theoretical insights with practical applications |
- Papers - Academic research papers on LLM safety and ethics
- Articles - Blog posts, news articles, and analyses
- Tools - Software tools and libraries for LLM safety
- Datasets - Relevant datasets for research and testing
- Organizations - Key organizations working on AI safety
- Glossary - Common terms in LLM safety and ethics
We welcome contributions from the community! Please read our Contributing Guidelines before submitting your contributions.
- Fork the repository
- Create your feature branch
- Commit your changes
- Push to the branch
- Create a Pull Request
- Join our Discord Server
This project is licensed under the MIT License - see the LICENSE file for details.