Discussion and research on AI alignment. alignmentforum.org
Presents a theory of change arguing value generalisation is central to alignment, that it aids capability gains, and that a corporate structure with an ethics board is safer for pursuing it.
Argues the safety-usefulness tradeoff model only fits developers who trade efficiently, and that under third-party pressure, safety strategy should weigh political feasibility instead.
Discusses how to safely defer to increasingly capable AIs for AI safety and strategy work, covering the basin of good deference, required alignment and capability profiles, and the lead time needed to manage exogenous risks.
Argues that high-reliability engineering practices like detailed specs and staged testing cannot straightforwardly transfer to AGI, since AGI behaviour is open-ended and out-of-distribution, so alignment needs deeper understanding.
Lays out a framework of Plans A to E describing how different levels of political will shape the best strategy for handling misalignment risk, with estimated probabilities and takeover risks for each.
An introductory 75-minute course from Google DeepMind's alignment team covering misalignment risk arguments, their technical alignment approach, interpretability, and institutional and governance practices such as dangerous capability evaluations.