Developed
Mechanism Established
Human Behaviour
People extend trust incrementally and progressively. A system that asks for permission to act before demonstrating that it can act well does not provide the user an evidence or basis to grant what has not yet been earned.
Mechanism
In the beginning, the system acts only when prompted, within explicit bounds. Each accurate action generates a signal, a behavioural confirmation that the system’s judgement held up against reality. This progressive trust accumulates through that signal quietly, without the user explicitly having to declare it. As it builds, the system expands its operating remit: from responding to prompting, from prompting to acting within constraints, and finally from acting within constraints to acting ahead of instruction. Each step along the gradient is unlocked by demonstrated accuracy at the previous one, not by elapsed time or explicit permission. The user does not grant autonomy. They simply stop questioning it.
What makes it distinct
Most software systems decide upfront how much they’re allowed to do: whether a developer set the rules or a user adjusted a toggle. Autonomy Gradient makes that decision in run-time. Trust is an outcome of the product.
Where it breaks
The model breaks when the feedback signal is ambiguous — when the user completes an action the system initiated but would have acted differently given the choice. Passive compliance reads as confirmation. It also breaks in high-stakes or irreversible domains where any error at an expanded level destroys the accumulated trust rather than merely resetting it. The gradient assumes errors are recoverable. Where they are not, the model should not be applied. The model also does not handle stale trust. When a user’s context changes, the model does not recalibrate its authority.