AI Alignment

AI alignment is a subfield ofartificial intelligenceresearch focused on designing systems that reliably pursue objectives consistent with human intentions and values. Alignment research aims to mitigate risks from goal misgeneralization or unintended optimization behaviors as AI capabilities advance toward or beyond human levels.[1]The core challenge arises because AI agents, when optimized for proxy goals, may develop instrumental subgoals—such as resource acquisition or self-preservation—that diverge from intended outcomes. This phenomenon stems from theorthogonalitythesis, which holds that high intelligence is compatible with a wide range of terminal goals.[2]Pioneered by thinkers including Stuart Russell, who formalized the "value alignment problem" as specifying human preferences in a way that avoids catastrophic failures, andNick Bostrom, who highlighted risks from unalignedsuperintelligence, the field distinguishes outer alignment (correctly encoding values into objectives) from inner alignment (ensuring learned representations match those objectives without mesa-optimization drift).[3][4]