Active Inference: A method for Phenotyping Agency in AI systems?
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
Full article excerpt tap to expand
Computer Science > Artificial Intelligence arXiv:2604.23278 (cs) [Submitted on 25 Apr 2026] Title:Active Inference: A method for Phenotyping Agency in AI systems? Authors:Philip Wilson, Axel Constant, Mahault Albarracin, Nicolás Hinrichs, Jasmine Moore, Daniel Polani, Karl Friston View a PDF of the paper titled Active Inference: A method for Phenotyping Agency in AI systems?, by Philip Wilson and Axel Constant and Mahault Albarracin and Nicol\'as Hinrichs and Jasmine Moore and Daniel Polani and Karl Friston View PDF HTML (experimental) Abstract: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 Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2604.23278 [cs.AI] (or arXiv:2604.23278v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2604.23278 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Philip Wilson [view email] [v1] Sat, 25 Apr 2026 12:41:53 UTC (198 KB) Full-text links: Access Paper: View a PDF of the paper titled Active Inference: A method for Phenotyping Agency in AI systems?, by Philip Wilson and Axel Constant and Mahault Albarracin and Nicol\'as Hinrichs and Jasmine Moore and Daniel Polani and Karl FristonView PDFHTML (experimental)TeX Source view license Current browse context: cs.AI < prev | next > new | recent | 2026-04 Change to browse by: cs References & Citations NASA ADSGoogle Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle…
This excerpt is published under fair use for community discussion. Read the full article at arXiv.org.
Discussion
0 commentsMore from arXiv.org