Collection Proposal: AI Safety & Alignment Ecosystem
Overview
This collection tracks the rapidly growing ecosystem of AI Safety, Alignment, and Trustworthy AI tools and frameworks. As LLMs become more capable, ensuring they remain aligned with human values and operate safely is critical.
Key Categories
1. RLHF & Human Feedback
- lucidrains/PaLM-rlhf-pytorch (7.8K⭐) - RLHF implementation on PaLM architecture
- opendilab/awesome-RLHF (4.3K⭐) - Curated RLHF resources
- huggingface/data-is-better-together (271⭐) - Community dataset building for human preferences
2. AI Alignment Frameworks
- alignment-handbook (5.5K⭐) - Recipes for aligning language models
- align-anything (4.6K⭐) - Aligning any language model with any preference
- OpenClaw-RL (4.1K⭐) - Open-source RLHF implementation
- PKU-Alignment/beavertails (178⭐) - Safety alignment datasets for LLMs
3. Safety & Moderation
- superagent-ai/superagent (6.5K⭐) - Protects AI apps against prompt injections and harmful outputs
- katanemo/plano (6.0K⭐) - AI-native proxy with built-in safety and orchestration
4. Interpretability & Mechanistic Analysis
- TransformerLensOrg/TransformerLens (3.2K⭐) - Mechanistic interpretability for GPT-style models
- cap (3.6K⭐) - Causal scrubbing for interpretability research
- polygraph (1.3K⭐) - LLM uncertainty estimation and hallucination detection
5. Trustworthy & Ethical AI
- trustworthy-ai (2.5K⭐) - Microsoft's trustworthy AI tools
- mlco2/codecarbon (1.7K⭐) - Track and reduce ML compute emissions
- EthicalML/awesome-artificial-intelligence-regulation (1.4K⭐) - AI guidelines, ethics, and regulations
6. Data Quality & Preference Learning
- argilla (4.9K⭐) - Collaborative data curation for AI
- distilabel (3.1K⭐) - Synthetic data generation for AI alignment
Why This Collection Matters
- Explosive Growth: AI safety has moved from academic research to production-critical infrastructure
- Regulatory Pressure: EU AI Act, ISO 42001, and other frameworks require demonstrable safety measures
- Enterprise Adoption: Companies need tools to ensure their AI systems are safe, aligned, and compliant
- Research Momentum: Major labs (Anthropic, OpenAI, DeepMind) are heavily investing in alignment research
Collection Metadata
- Proposed Collection Name:
ai-safety-alignment
- Estimated Repos: 20-30 high-quality repos
- Total Stars: 50K+ combined
- Growth Trajectory: Rapid (new frameworks monthly)
- Priority: High - complements existing AI infra collections
Related Existing Collections
This collection fills the gap for technical alignment tools - the actual frameworks and libraries used to make AI systems safer and more aligned.
Collection Proposal: AI Safety & Alignment Ecosystem
Overview
This collection tracks the rapidly growing ecosystem of AI Safety, Alignment, and Trustworthy AI tools and frameworks. As LLMs become more capable, ensuring they remain aligned with human values and operate safely is critical.
Key Categories
1. RLHF & Human Feedback
2. AI Alignment Frameworks
3. Safety & Moderation
4. Interpretability & Mechanistic Analysis
5. Trustworthy & Ethical AI
6. Data Quality & Preference Learning
Why This Collection Matters
Collection Metadata
ai-safety-alignmentRelated Existing Collections
This collection fills the gap for technical alignment tools - the actual frameworks and libraries used to make AI systems safer and more aligned.