Safety Responses
Overview
Section titled “Overview”This section documents interventions and approaches being developed to address AI safety risks. Responses span technical research, governance mechanisms, institutional development, and public engagement.
Response Categories
Section titled “Response Categories”Research aimed at ensuring AI systems behave as intended:
- Mechanistic Interpretability - Understanding model internals
- RLHF - Reinforcement learning from human feedback
- Constitutional AI - Training with explicit principles
- AI Control - Limiting AI autonomy regardless of alignment
- Evaluations - Testing for dangerous capabilities
Policy and regulatory approaches:
- Compute Governance - Controlling AI through hardware
- Export Controls - Restricting chip access
- Responsible Scaling Policies - Lab commitments
- Legislation - Government regulation
Organizations and structures for AI safety:
- AI Safety Institutes - Government research bodies
- Standards Bodies - Technical standard development
Technologies to preserve information integrity:
- Coordination Technologies - Enabling cooperation
- Content Authentication - Verifying authentic media
- Prediction Markets - Aggregating forecasts
Growing the AI safety research community:
- Training Programs - Researcher development
- Corporate Influence - Engaging industry
Evaluating Responses
Section titled “Evaluating Responses”Each response page includes assessments of:
- Tractability - How feasible is progress?
- Neglectedness - How much attention is it getting?
- Potential Impact - How much could it help if successful?
See the Intervention Portfolio for comparative analysis.