Rigorous Alignment vs. Heuristic Preferences
Traditional alignment techniques often rely on Reinforcement Learning from Human Feedback (RLHF), which optimizes for statistical preferences. Formal AI safety utilizes Formal Methods to provide mathematical guarantees that system behavior adheres to predefined safety bounds.
Key Methodologies
- Safe Reinforcement Learning: The integration of hard constraints into the reward function or policy space to prevent unsafe state transitions during the learning process.
- Certified Robustness: Mathematical techniques to prove that a model's output remains within a specified range under input perturbations.
- Formal Shielding: An external monitor that evaluates proposed actions against a formal specification, intercepting and correcting actions that would violate defined safety invariants.
Foundational Research
- The Three Laws of Robotics: While science fiction, Asimov's laws served as the early conceptual precursor to the "value alignment" problem in modern AI safety.
- Concrete Problems in AI Safety (2016): A milestone paper that defined practical research directions like avoiding side effects and safe exploration.
- Superintelligence (Bostrom): A foundational philosophical work that highlighted the risks of unaligned intelligence and the importance of formal control mechanisms.
- Center for Human-Compatible AI (GitHub): A leading research center focusing on mathematical foundations for beneficial AI.
Operational Objective
The research focus is the transformation of safety requirements into Formal Specifications. By using verification tools like TLA+ or Alloy, it is possible to model agentic workflows and prove adherence to alignment protocols by design.