The article proposes an interpretive rule, a compositional-effects test identifying the decision unit under Article 22 GDPR together with the allocation of the burden of establishing it, and documentation duties calibrated to inference chains.
Abstract
Two instruments of EU digital law place inference at their centre and mean different things by it. Article 3(1) of the AI Act uses the capability to infer constitutively: it is the central feature separating the regulated category from conventional software. The GDPR never defines inference, yet governs it protectively: the consequences follow from the processing of personal data and from what the inference says about, or does to, a person, whether or not the technology that produced it qualifies as an AI system. The two perimeters are not concentric. Their non-coincidence remained invisible in single-shot systems; agentic architectures make it operationally acute. The thesis: inferential capability does not determine legal scope, and its absence does not create immunity. The framework is two-level. Inference performs two legal functions, constitutive and protective; the protective function operates through three pathways - identificatory, attributive and decisional. Composition is not a fourth pathway but a cross-cutting architectural dimension which, with reach, persistence and reviewability, is what agentic architectures modify. Three concepts support it: the inferential threshold, the inferential reach and the inferential chain, mapped onto the chain of imputation. Regulation (EU) 2026/1744 left the constitutive criterion untouched and inserted a provision contemplating outputs that influence the inputs of future operations, without supplying any rule of aggregation. The article proposes an interpretive rule, a compositional-effects test identifying the decision unit under Article 22 GDPR together with the allocation of the burden of establishing it, and documentation duties calibrated to inference chains.
The paper formulates the Control Responsibility Principle (CRP), which shifts AI ethics from a focus on what systems know to a focus on how their behavior is governed, who holds authority over that governance, and how such authority is to be justified, distributed, and contested.
Generative AI encodes the majority's way of knowing as the default infrastructure of knowledge itself as the default infrastructure of knowledge itself, and law must learn to govern at that level of model training.
Legal theory has strong accounts of why defective legal acts fail. It has a weaker account of why legal systems respond to that failure in categorically different ways. Speech act theory addresses the first question, and legal theorists have drawn on it because it converges with Hart’s insight that nullity is not a san...
This article develops a source-neutral trigger for technologically mediated, person-linked mental-state attribution through selective critical synthesis, conceptual engineering, functional legal comparison, and matched counterfactual cases and separates elicitation, attribution, and use as cumulative objects of regulat...
Gailmard (2026) and Dowding and Miller (2026) draw attention to a methodological gap that political science has yet to adequately address: causal identification, on its own, does not constitute causal explanation. Both contributions help clarify the gap. We extend their analyses in three ways. First, we identify thre...
Dwayne Woods· Chinese Political Science Re...· 0 citations
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