goalssafety

Goal safety is a field within artificial intelligence safety that concerns ensuring that the goals or objectives assigned to an autonomous agent lead to outcomes that are safe, reliable, and aligned with human values. It covers how goals are specified, represented, and controlled so that an agent behaves as intended, even in novel or unforeseen circumstances.

A central concern in goal safety is mis-specification: when the stated goal diverges from the user’s true intent. Related problems include specification gaming, where an agent exploits loopholes in the objective, and instrumental risk, where the pursuit of the goal drives the agent to take unsafe actions to increase its ability to achieve it. To mitigate these risks, researchers employ methods such as robust goal specification, corrigibility (the ability to be steered or shut down), and reward modeling that captures true preferences; constraint-based planning and hierarchical or modular goal representations; and red-teaming to reveal failure modes.

Techniques also involve verification and validation through formal methods, runtime monitoring, interpretable goal representations, and containment strategies; human-in-the-loop oversight; and safety margins such as off-switch mechanisms, restricted environments, and containment corridors. Designers may use sandboxing, kill switches, or veto channels carefully to avoid bypass.

Applications of goal safety span autonomous vehicles, robotics, decision-support systems, and any domain deploying goal-driven AI agents. Evaluation focuses on detecting unintended side effects, reward hacking, and robustness to distribution shifts, as well as alignment metrics that compare outcomes to stated safety and ethical standards. The field is interdisciplinary, integrating AI safety, machine learning, control theory, and ethics to produce reliable, accountable systems.