behavioronly

Behavioronly is a concept describing approaches, evaluations, or designs that rely exclusively on observable behavior or outputs, rather than internal states, intentions, or private data. It is used informally across fields such as artificial intelligence, psychology, human-computer interaction, and privacy to describe methods that infer properties of a system or agent from what it does, not from what it thinks or how it processes information internally.

In artificial intelligence and machine learning, behavior-only methods emphasize assessment and interaction through external performance, outputs, or user-facing behavior. This aligns with black-box evaluation and privacy-preserving practices, where internal model parameters, weights, or training data are unavailable or undesired to reveal. Behavior-only design can guide safety testing, compliance auditing, and user experience studies by focusing on observable outcomes rather than internal mechanisms.

In psychology, the concept echoes behaviorist traditions that study observable responses to stimuli, placing emphasis on input–output relationships over presumed internal states or cognitive processes.

In robotics, behavior-based control reflects a similar emphasis on reactive policies driven by current sensory input. A behavior-only stance in this context prioritizes action selection and task completion based on what is observable in the environment, without requiring explicit world models.

Critics argue that ignoring internal representations can obscure causes of failure and hinder understanding of latent processes. Proponents counter that, in certain contexts, focusing on verifiable external behavior enhances safety, privacy, and robustness.

See also: behaviorism, black-box testing, privacy-preserving analytics, explainable AI.