A field of study and engineering practice focused on ensuring that artificial intelligence systems behave in ways that reliably reflect human intentions, values, and safety constraints, particularly as systems become more autonomous and capable. Alignment seeks to address the risk that highly competent models might pursue goals that are technically correct but socially harmful, misinterpreting instructions or optimizing for unintended outcomes. Techniques include reinforcement learning with human feedback, interpretability research, constraint design, and oversight mechanisms that keep systems accountable to human judgment.
The term gained prominence through AI safety research communities and scholars such as Stuart Russell, who argue that increasingly powerful systems must be designed to remain corrigible and deferential to human control. In broader cultural debates, AI alignment has become shorthand for the central question of the automation age, namely whether intelligent machines will amplify human flourishing or drift toward outcomes misaligned with societal needs, making it a foundational concern for governance, ethics, and long-term technological risk.