The delegation illusion: why deploying autonomous AI agents does not diminish principal responsibility

Abstract

A growing literature on “agentic AI” — autonomous software agents that plan and execute multi-step actions on a principal’s behalf — has revived the thesis that such systems open a responsibility gap: because the deploying principal neither intends, foresees, nor controls the specific actions an autonomous agent selects, no human can be held fully responsible for resulting harms. This paper argues that the responsibility-gap thesis, as applied to principal-deployed AI agents, rests on a conflation called here the delegation illusion: the inference from a principal’s causal and epistemic remoteness from an outcome to a diminution of her answerability for it. Distinguishing attributability, answerability, and accountability, the paper argues that autonomy, opacity, and adaptivity bear on attributability while leaving intact an answerability grounded, through a standard tracing structure, in the deployer’s guidance control over the prior act of delegation. It defends a principle of responsibility conservation under delegation and operationalises that principle’s central criterion — foreseeability of harmful dispositions at the level of types — by specifying an epistemic base, a standard of diligence, and a constraint on type individuation that blocks vacuous redescription. Answerability is argued to be epistemically graded, but indexed to the deployer’s ex ante access to the dispositional profile of the deployment specification rather than to the particular act. The method is conceptual analysis and normative argumentation; the scope and limitations of the claims are stated explicitly.