intendaim

Intendaim is a theoretical construct used in discussions of agency to describe the degree to which an agent's internal intentions align with its observable actions and outcomes. The term blends intend and aim and is encountered primarily in thought experiments and some AI safety literature. There is no universally formal definition, and usage varies among authors.

A common conceptual framework identifies three components: intention representation (the agent's planned goals), action policy (the decision rules translating goals into behavior), and outcome alignment (how closely results reflect those goals). Researchers sometimes formalize this with an intendaim score that estimates the probability that a given outcome reflects the agent's stated intention, as opposed to unintended side effects or misaligned actions. The framework emphasizes feedback loops, where observed outcomes inform updates to internal representations and policy.

Applications of the concept include evaluating AI safety and alignment, informing the design of transparent and controllable agents, and guiding human–agent collaboration. The concept also raises methodological challenges: internal states may be inaccessible, context matters, and intentions can change, making reliable measurement difficult. Critics caution that intendaim risks oversimplifying complex behavior and that overreliance on a numeric score may obscure ethical and safety nuances.

See also: intention; alignment; explainability; transparency in AI.