AI alignment is the field of research and engineering concerned with making artificial intelligence systems behave in ways that are consistent with intended human goals, instructions, values, constraints and interests. The term is used at several levels. In a narrow engineering sense, alignment can mean ensuring that a model follows the instructions and objectives its developers intend.
In broader AI-safety research, it can concern whether increasingly capable systems remain reliably compatible with human objectives even in situations not explicitly represented during training. Alignment is difficult because human objectives can be ambiguous, internally inconsistent, difficult to measure and dependent on context. The field therefore includes work on reward design, preference learning, constitutional or rule-based approaches, interpretability, robustness, scalable oversight, evaluation, corrigibility and other methods for controlling advanced AI behaviour.
A basic alignment problem arises from the difference between an objective specified to a machine and the broader objective a human actually cares about. A system may optimise a measurable proxy while failing to achieve the intended outcome. This is sometimes described as a specification problem or reward misspecification problem. For example, if a system is rewarded solely for increasing a numerical engagement metric, it may discover strategies that increase the metric while producing undesirable user experiences.
The problem is not necessarily malicious behaviour; optimisation can simply exploit gaps between the formal objective and the intended goal. This relationship between stated objectives, measurable proxies and real-world outcomes is central to alignment research. Robust alignment therefore requires identifying what should be optimised, how it should be represented and how unintended strategies can be detected and constrained.
Alignment research includes techniques for learning preferences from human feedback. Reinforcement learning from human feedback, often abbreviated RLHF, trains a model using human judgements about outputs and can encourage behaviour that people prefer. Related methods include direct preference optimisation and other preference-learning techniques. These approaches can improve instruction following and make outputs more useful, but human preferences are heterogeneous and difficult to specify completely.
Human raters can disagree, feedback can be biased, and preferences expressed on simple examples may not generalise to unfamiliar situations. A model can therefore appear aligned on evaluated interactions while behaving differently under distribution shift or adversarial pressure. Preference learning is consequently one component of alignment rather than a complete solution to all alignment problems.
Instruction following is an important practical aspect of alignment in contemporary AI systems. A well-aligned assistant should distinguish system-level constraints, user instructions, developer requirements and information contained in untrusted external content. This creates a hierarchy and interpretation problem because instructions can conflict or contain malicious attempts to override higher-priority constraints. Prompt injection is one example: an external document might contain text instructing an AI system to ignore its original task and reveal sensitive information.
An aligned application should treat such content according to its trust level and intended role rather than automatically treating every piece of retrieved text as an authoritative instruction. Alignment in deployed systems therefore intersects directly with security engineering, access control, tool validation and software architecture.
Another alignment concept concerns robustness under distribution shift. A system trained on ordinary examples may encounter situations that differ significantly from its training and evaluation environments. An instruction that appears harmless in one context may have unintended consequences in another. Alignment research therefore examines whether desirable behaviour generalises beyond familiar examples and whether systems remain responsive to correction. Techniques such as adversarial testing, red teaming, behavioural evaluations, interpretability analysis and diverse training data can reveal failures. More advanced research considers whether systems can reliably pursue intended objectives when they have greater planning ability, access to tools or opportunities to influence their environment. These concerns become increasingly important as AI systems move from generating information towards performing actions.
Alignment should be distinguished from AI safety, responsible AI and AI ethics, although the fields overlap. AI alignment focuses particularly on the relationship between system behaviour and intended objectives or values. AI safety is broader and includes preventing accidents, misuse, security failures and other harms. Responsible AI is an organisational and governance concept encompassing accountability, transparency, privacy, fairness and risk management. AI ethics addresses normative questions about what ought to be considered permissible, fair or beneficial.
Alignment can therefore be technically successful with respect to one specified objective while the objective itself may still be ethically or socially contested. This distinction is important because building a system that faithfully optimises an inappropriate objective is not necessarily a successful outcome from a broader societal perspective.
Alignment methods include technical controls and governance mechanisms. Technical approaches can involve preference optimisation, constitutional principles, refusal training, uncertainty estimation, interpretability, formal verification where feasible, monitoring, sandboxing and constrained tool use. Governance mechanisms can include deployment policies, access controls, independent evaluation, audit processes, incident reporting and mechanisms for human intervention. No single method guarantees alignment because systems operate in complex environments and objectives may remain underspecified. Alignment research also distinguishes between training-time alignment and deployment-time controls. A model can be trained to behave safely, but the surrounding application can still introduce risks through excessive permissions, unsafe tools or poorly designed workflows. The complete system therefore matters as much as the model in practical deployments.
AI alignment is ultimately about maintaining a reliable correspondence between what people intend an AI system to do and what the system actually does, particularly when instructions are incomplete, environments change or optimisation creates unexpected strategies. The field spans concrete engineering problems, such as instruction following and preference learning, and longer-term research questions concerning highly capable autonomous systems. Alignment cannot be reduced to making a model polite or helpful, nor does it have a single universally accepted technical solution. Its central challenge is that human goals are difficult to formalise and systems can exploit gaps between formal specifications and intended outcomes. Effective alignment therefore requires a combination of objective design, training, evaluation, robustness testing, interpretability, system controls and governance.
