Intentionality is a Design Decision: Measuring Functional Intentionality for Accountable AI Systems

TL;DR

This paper introduces a framework to measure and quantify the intentional-like behavior of AI systems, aiding governance and accountability by assessing design-contingent properties.

Contribution

It defines intentionality in AI as a behavioral profile and proposes the Functional Intentionality Test (FIT) for measurement and evaluation.

Findings

Proposes the FIT framework with five observable dimensions.

Introduces FIT-Eval for structured intentionality assessment.

Highlights the importance of measuring intentionality for accountability.

Abstract

As AI systems increasingly exhibit autonomous, goal-directed, and long-horizon behavior, users lack a standardized way to detect the degree to which a system functions like an intentional actor for governance and accountability purposes. This position paper defines intentionality not as consciousness, but as a behavioral profile characterized by purpose, foresight, volition, temporal commitment, and coherence - criteria long used in legal and philosophical contexts to infer intent. These properties are design-contingent: architectural choices such as memory persistence, planning depth, and tool autonomy shape the degree to which systems exhibit organized goal pursuit. If intentionality is design-contingent, it is in principle controllable. Yet control requires measurement. We introduce the Functional Intentionality Test (FIT), a multidimensional framework that quantifies intentional-like behavior across five observable dimensions, and propose FIT-Eval, a structured evaluation protocol for eliciting and scoring them. While reduced human agency can increase efficiency, rising intentional capacity heightens accountability risks. By translating intentionality into interpretable levels, FIT enables proportionate oversight and deliberate autonomy calibration in increasingly agentic systems.