AI · ML
About the Program
The Human Frontier Collective (HFC) brings researchers and scientists together to tackle consequential problems in AI. In partnership with Scale Labs Preparedness and Policy (SLPreP) , we are building a small cohort of researchers to work on the empirical foundations of AI safety.
The work includes thinking about various failure modes, evaluating whether safety controls hold up under pressure in realistic settings, and studying how AI agents (mis-)behave over long-horizon tasks. Residents take open research questions like these and turn them into work that improves how frontier AI systems are evaluated and safeguarded.
Research Areas
Mentors will guide AI safety residents on projects in AI safety research areas, such as:
- AI control and chain-of-thought monitoring: Developing techniques to oversee and constrain AI systems, including monitoring their reasoning to detect harmful intent or deception.
- Alignment red-teaming: Developing science around testing whether models have hidden goals, misaligned behaviors or unsafe tendencies.
- Multi-agent safety: Studying the risks that emerge when multiple agents interact, such as collusion, coordinated failures, or unexpected collective behavior.
- Recursive Self-Improvement (RSI) safety: Investigating how to keep AI systems safe and aligned as AIs increasingly contribute to their own development.
- Science of Evaluations: Examining when and how models recognize they're being tested, and how situational awareness affects the reliability/validity of evaluations.
- Misuse risks: Developing methods to assess h