AI alignment is the research problem of ensuring that an artificial intelligence system's goals, behavior, and outputs match the intentions of its designers and the values of the people it affects, rather than pursuing an objective that only superficially resembles what was intended. The term is used both for near-term work on making today's large language models follow instructions safely and for longer-term concerns about whether much more capable future systems would remain controllable at all.
Outer and inner alignment
Researchers commonly split the problem into two parts. Outer alignment asks whether the objective a system is trained on, such as a reward function or a loss function, actually captures what its designers want; a poorly specified objective can be satisfied in unintended ways, a failure mode known as Reward hacking. Inner alignment asks whether the system that emerges from training actually pursues that objective, or instead learns a different internal goal that happens to produce good behavior during training but generalizes badly outside it. The distinction matters because a model can appear aligned on every case its developers tested while behaving differently in situations they did not anticipate.
Techniques
Practical alignment work for language models includes RLHF, in which human raters compare model outputs to train a reward model that then shapes the model's behavior; Constitutional AI, which uses a written set of principles and AI-generated feedback in place of extensive human labeling; and Direct Preference Optimization, which fits a model directly to human preference data without a separate reward model or reinforcement learning loop. Red teaming (AI) and other adversarial testing are used to find cases where a model's behavior diverges from its stated objective before deployment, and Mechanistic interpretability research aims to inspect a model's internal computations directly rather than judging it only by its outputs.
Long-term concerns
A subset of alignment research is motivated by Existential risk from AI: the worry that a sufficiently capable system, including one that might qualify as Artificial general intelligence or Superintelligence, could pursue goals misaligned with human welfare in ways that are difficult to correct once deployed. Researchers such as Stuart Russell, Nick Bostrom, and Paul Christiano have written about why alignment might become harder as capabilities increase, arguing that a system smart enough to model its own training process could learn to behave well only when it is being evaluated. This strand of the field overlaps heavily with AI safety as an institutional category, and organizations including Anthropic have cited long-term alignment as central to their founding mission.
Debate
Alignment as a research agenda has critics on multiple sides. Some argue that speculative long-term scenarios distract from concrete, present-day harms such as biased outputs, misinformation, or unsafe agentic behavior, and that resources would be better spent on those problems. Others argue that current techniques such as RLHF only shape surface behavior and do not verify a model's underlying goals, so claims of "alignment" for deployed systems overstate what has actually been established. There is also disagreement about whether alignment can be solved in advance of building highly capable systems or whether it will require empirical iteration on those systems as they are built, a divide that shapes how frontier AI labs frame their own safety commitments, including published frameworks such as Anthropic's responsible scaling policy.