AI agent safety and alignment research, mapped
The article maps the landscape of AI agent safety and alignment research, covering key challenges such as goal misalignment, specification gaming, and corrigibility, and surveys major approaches including oversight techniques, interpretability, and value learning to ensure advanced AI agents act reliably and ethically.
Background
- A visual map of the AI agent safety and alignment research field, categorizing organizations, research groups, and core problems.
- "AI agents" are autonomous systems that act on a user's behalf (execute code, browse the web), unlike simple chatbots.
- "Alignment" means ensuring AI systems do what humans actually intend, especially as they grow more capable.
- Core concerns shown: specification gaming (loopholes), reward hacking, goal misgeneralization, multi-agent dynamics.
- Key orgs: DeepMind, Anthropic, MIRI, ARC, and academic labs like UC Berkeley's CHAI.
- Field exploded post-2022 as LLMs (GPT-4, Claude) became the "brains" of autonomous agents.