Active Inference: A method for Phenotyping Agency in AI systems?
This work provides a principled framework for phenotyping agency in AI systems, which could inform governance strategies, but the empirical demonstration is limited to a simple toy task.
The authors propose a minimal definition of agency based on intentionality, rationality, and explainability, instantiated as a partially observable Markov decision process under active inference. They demonstrate that empowerment, measured as channel capacity, can distinguish zero-, intermediate-, and high-agency phenotypes in a T-maze task.
The proliferation of agentic artificial intelligence has outpaced the conceptual tools needed to characterize agency in computational systems. Prevailing definitions mainly rely on autonomy and goal-directedness. Here, we argue for a minimal notion open to principled inspection given three criteria: intentionality as action grounded in beliefs and desires, rationality as normatively coherent action entailed by a world model, and explainability as action causally traceable to internal states; we subsequently instantiate these as a partially observable Markov decision process under a variational framework wherein posterior beliefs, prior preferences, and the minimization of expected free energy jointly constitute an agentic action chain. Using a canonical T-maze paradigm, we evidence how empowerment, formulated as the channel capacity between actions and anticipated observations, serves as an operational metric that distinguishes zero-, intermediate-, and high-agency phenotypes through structural manipulations of the generative model. We conclude by arguing that as agents engage in epistemic foraging to resolve ambiguity, the governance controls that remain effective must shift systematically from external constraints to the internal modulation of prior preferences, offering a principled, variational bridge from computational phenotyping to AI governance strategy